Measuring activity spaces of people, households and
population segments
Catherine Morency, P.Eng., Ph.D., assistant professor
Groupe MADITUC
Centre de recherche interuniversitaire sur les réseaux d’entreprise, la logistique et le
transport (CIRRELT)
Department of Civil, Geological and Mining Engineering
Ecole Polytechnique de Montréal, P.O. box 6079, station Centre-Ville, Montréal, Qc,
CANADA, H3C 3A7
email : cmorency@polymtl.ca
Yan Kestens, Ph.D.
Direction de Santé Publique de Montréal, research assistant professor
Département de médecine sociale et préventive, Université de Montréal
1301, rue Sherbrooke est, Montréal, Qc
CANADA, H2L 1M3
email : yan.kestens@umontreal.ca
1
Measuring activity spaces of people, households and
population segments
Catherine Morency and Yan Kestens
Abstract
This paper contributes to the discussions on activity location choice modelling by
examining the concept of activity spaces. Actually, data from large scale OriginDestination surveys held in 1998 and 2003 in the Greater Montreal Area are used for
the first time to quantify the use of space of more than 165,000 people nested in
some 65,000 households. Systematic measures of daily activity spaces of individuals
and households show that the number of locations used for daily activities have
decreased during the 5 year period for every segment but that the area used to
conduct these activities have increased.
Acknowledgments
The authors wish to acknowledge the transport authorities responsible for the
conduct of large scale Origin-Destination surveys in the region, namely the AMT,
MTQ, STM, STL and RTL.
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Measuring activity spaces of people, households and
population segments
1.
Introduction
Urban sprawl, socio-demographic evolution and car accessibility have modified the
way people organize their daily behaviours in space and time. New concepts such as
activity spaces have emerged to improve our understanding of the spatial patterns of
individual travel behaviours. Activity spaces relate to “that part of the environment,
which a traveller is using for his/her activities” (Axhausen et al., 2004). They can be
assessed with various indicators, using confidence ellipses, kernel densities or
convex hulls for instance. Still, the precise quantification of these activity spaces, for
various population groups, requires significant amount of data on the activity
behaviour. In order to contribute to the discussions on activity location choice, data
from large scale Origin-Destination surveys held in 1998 and 2003 in the Greater
Montreal Area are used, for the first time, to quantify and analyse the use of space of
more than 165,000 people nested in some 65,000 households.
The paper is organised as follows. First, research on the related domains is discussed.
Then, the information system used to quantify the activity spaces of people and
households is defined. Origin-Destination datasets are described as well as the
concepts used to quantify activity spaces. Results issued from the processing of the
two datasets are then presented and discussed. Research perspectives conclude the
paper.
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2.
Background
The modelling of activity location choice is still a big concern in the transport
community. Up until recently, the spatial distribution of destinations was often
neglected. However, notable work has been done in this domain in the recent years,
mainly due to the important advances in spatial technologies and GIS (Geographic
Information Systems). The growing availability of precisely geocoded databases
regarding individual behaviours in space and time is also responsible for the
emergence of multiple discussions on the topic. Consequently, concepts such as
activity spaces or space-time prisms have emerged in order to enhance our
understanding of activity-related travel behaviour, space consumption, and time
management. A concomitant goal was to refine measures of accessibility. Literature
on activity space often refer to the space-time prism of Hägerstrand (1970) which
illustrates how a person navigates his or her way through the spatial-temporal
environment. Many contributions were done by Kwan (1998, 1999, 2000) on the
representation of activity spaces (concepts, tools and visualisation) and by Miller
(1999, 2004) on accessibility measures.
The observation of travel behaviours, in space and time, requires precise and
exhaustive datasets (the lack of which explains why few works relate quantitative
measures of the set of locations used by people and households). Numerous
researches are however done to elaborate appropriate survey methods. Some authors
point to the need to develop survey methods particularly able to unravel the activity
planning process (Kreitz and Doherty, 2002 for instance). Incidentally, a new facet of
activity location choice modelling has emerged in the recent years integrating the
notion of social networks. One aim is to account for people’s social interactions in
the acquisitions of activity locations. Methods pertaining to the analysis of social
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networks are integrated to activity location choice modelling for this purpose (see for
instance Carrasco and al., 2006, Axhausen, 2005 and 2006).
Modelling the activity patterns of individuals while taking into account the
household context is also part of current discussions; it is often referred to as withinhousehold interactions. Miller and Roorda (2003) present a household activity-travel
scheduler calibrated using trip diary. Other research on households’ activity are
discussed by Wets et al. (2000), Vadarevu and Stopher (1996), Jun and Goulias
(1997).
Some methodological and technological contributions are worth noticing. Buliung
and Kanaroglou (2004) present an object-oriented, GIS-based environment that
facilitates the visualisation, exploration and description of households’ activity
patterns. Various measures can be compiled using this tool: central tendency and
dispersion, convex hull and three-dimensional space-time paths. Free tools such as
CrimeStat (Levine, 1996 and 2000) and GeoDa (Anselin et al., 2006) clearly help
disseminate knowledge regarding spatial statistics.
As mentioned earlier, even if activity location choice has been a critical modelling
issue for some time, few works relate quantitative measures of activity spaces. From
these, the various measures conducted using the Mobidrive six-week diary are worth
noticing. Actually, such data offer a lot of observation for a particular unit of
observation (a person) and allows measuring the variability of travel behaviour in
space and time. Various works were conducted using this dataset: Schönfelder and
Axhausen (2002) measure individual activity spaces using a series of measures,
Srivastava and Schönfelder (2003) show the temporal variability of activity spaces,
Kitamura et al., (2006) analyse the day-to-day variability of individual space-time
5
prisms, Susilo and Kitamura (2005) use the data to examine the features of
individual’s action space and measure day-to-day variability.
Walker (2004) discusses the difficulties of specifying the set of relevant alternatives
for location choice modelling and explores the potential of parameterizing activity
spaces instead of considering the whole area to define choice set. She demonstrates
that there is a strong relation between the home-work corridor and the location of
non-work activities. Kitamura et al. (1998) develop multinomial logit destination
choice model and show that time of day, duration of activity and home location
affect destination choice behaviours.
Buliung and Kanaroglou (2006) examine the spatial characteristics of daily
household activity-travel behaviours using an activity-travel survey from Portland,
Oregon. They show that urban households do less daily travel and have smaller
activity spaces.
From this review, we see that the processing of large scale travel surveys to measure
individual and household activity spaces is quite new and that there is a lack of
quantitative measurement of this use of space. The current research is a primer to the
systematic measure of activity spaces of people and households from two large scale
surveys held in the Montreal Area.
3.
Information system
The Greater Montreal Area GMA has a long tradition of conducting large scale travel
Origin-Destination (OD) surveys. Actually, OD surveys have been conducted
regularly in the region since the early seventies. These surveys sample approximately
5% of the residing population and gather data regarding a weekday of urban travel.
The interviews are conducted by phone and assisted by a CATI system since 1998
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(Chapleau et al., 2001). Information on households (size, car ownership, home
location, mobility), people (age, gender, driving license, main occupation) and trips
for all household members (time of departure, purpose, mode sequence, transit path,
highways/bridges, type of parking spaces, …) are gathered. Every interview relates
to a specific weekday of the fall period (September to December). Details regarding
these Origin-Destination household surveys can be found on the CIMTU web site
(http://www.amt.qc.ca/cimtu).
The OD datasets gather precisely geocoded information on the daily behaviour of
residents and allow the analysis, in space and time, of their activities. Figure 1 gives
a good idea of the spatial and temporal level of detail available in such datasets. This
figure shows six temporal snapshots of the spatial distribution of people over the
5000 km2 area during a typical weekday.
Figure 1. Chronology of the spatial location of mobile population during an average weekday
(1998) – square kilometre cells (Morency and Chapleau, 2004)
The richness of these surveys has often been demonstrated with respect to the
modelling of travel behaviours (see for instance Morency 2004 and 2006).
The current paper proposes new uses of the spatial data available regarding trips ends
and seeks to validate its capacity to describe activity spaces of people and households
during a typical day of fall. Actually, the two latest travel survey samples are
processed for this purpose. Table 1 presents summary statistics for the two latest
survey samples that are used for the analyses. As shown, the study of activity spaces
will rely on the examination of the daily behaviours of more than 370 000 trips for
which origin and destination points are precisely geocoded in space. The proportion
of active people and households identifies the portion of the sample for which
activity spaces can be examined (people are considered active if they did at least one
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trip during the day they were surveyed, households are considered active if at least
one of their member is active). Actually, the enumeration of activity location is
possible for every active people. However, the measure of activity spaces using
convex hull will be possible only for people or household visiting more than two
locations.
Table 1. Summary figures regarding the 1998 and 2003 travel survey samples
4.
Measures of activity spaces
Measures of activity spaces aim to summarize the use of the spatial environment of
travellers as they conduct their activities. These measures are based on the set of
points where activities are conducted, and may concern frequency, form, or network
(routes) characteristics. The spatial measures presented below are computed using
GIS tools (ArcGIS, PostGreSQL). Only urban locations within the survey area are
considered for these estimations.
As a former attempt to describe activity spaces of people and households using large
scale travel surveys, the following measures are estimated both at the individual and
household levels:

Enumeration of activity locations: this consists in identifying the set of
unique locations used, notwithstanding the number of times that it was visited
during the day. This list includes the home location when it was observed at
least once as origin or destination point. It is generally the case.

Area and perimeter of convex hulls (CH): in the plane, the CH can be
visualized as the shape assumed by a rubber band that has been stretched
around a set of points and released to conform as closely as possible to this
set. GIS functions are used to estimate the convex hull around the set of trip
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ends of travellers. Area ( ACH ) and perimeter ( PCH ) are computed for this
polygon. The Gravelius compactness coefficient is also estimated using area
and perimeter as follows: CI 
P
2 A
 0.28
P
A
. It approaches 1 when the
shape of the CH is almost circular and increases with the elongation. Convex
hulls with non-null areas can be estimated when the number of points is
superior to 2. Corresponding measures were therefore computed for a subset
of individuals and households which visited more than two locations.
A schematic illustration of these concepts follows (see Figure 2).
Figure 2. Schematic illustration of the activity spaces of people and households

Eccentricity of CH with respect to home location: the mean centre of the
polygon ( xCH , yCH ) expressing the CH is also estimated using GIS function.
The distance between home location and the mean centre of the CH gives a
first impression of the eccentricity of the activity locations with respect to
home location. Its value does not inform on the spatial dispersion of the
activity locations, it indicates whether the home location is central to the set
of points used for out-of-home activities.
5.
Results
5.1
5.1.1
Activity spaces of individuals
Enumeration of activity locations
Table 2 presents the average number of locations visited by a person during a typical
weekday, and this for various population segments. Since it was estimated for both
survey samples, it gives a first view of the evolution of activity spaces from 1998 to
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2003. The main observation it that the number of locations has decreased for all the
population segments and confirms that behaviours have evolved in the area.
Table 2. Average number of locations visited by a PERSON during a typical weekday (1998 and
2003)
As shown in Figure 3, the study of this indicator by age cohort and gender shows
decreases in values for all groups. Women between 30 and 44 years old are those
who visit the most locations per weekday.
Figure 3. Average number of locations visited by weekday according to age and gender (1998
and 2003)
The distribution of people according to the number of locations they visit by
weekday finally allows seeing that the majority of them (around 70%) use only two
different locations by days, one of them generally being the home location.
Consequently, more than two thirds of the active people do only two trips per
weekday. Figure 4 shows the frequency distribution for some distinctive population
segments. For instance, more than 80% of the students visit only two locations by
weekday while people owning a car are near 60% to visit only two locations.
Figure 4. Frequency distribution of the number of activity locations visited on an average
weekday for various population segments (2003 OD survey)
5.1.2
Features of convex hulls
Area and perimeter have been estimated for 33 075 (862 100 weighed people) and
45 123 (958 300 weighed people) people for the 2003 and 1998 survey samples
respectively. The resulting measures are further discussed.
The following table presents summary results regarding the size of convex hulls for
various population segments. First, we observe that activity spaces have become
wider for all population segments. We also see that some population segments have
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wider activity spaces than others. Actually, activity spaces increase as people live
further; they also are wider for men, owner of a car and workers. Car unavailability
seems to be the most important constraint to spatial dispersion of activity points.
Compactness coefficients do not vary significantly between population segments.
Home location appears to have the largest impact on the shape of activity space with
average compactness coefficient varying between 1.446 for people residing in the
CBD (Central Business District) to 1.804 for people living in the furthest suburbs.
Table 3. Summary features of the convex hulls representing activity spaces of PEOPLE during
a typical weekday (1998 and 2003)
Average features of convex hulls do not however reflect the variability of the activity
spaces of individual. Actually, in 2003, more than 25% of the people have convex
hulls’ areas smaller than 1 square kilometre and 50% smaller than 5 square
kilometres. Hence, convex hulls are quite variable. The following figure (Figure 5)
shows the cumulative distribution of people of various segments according to the
area of the convex hull. This distribution facilitates the observation of differences
between these segments. From this graph we observe that more than 70% of the
people without a car have CH smaller than 5 square kilometres while this figure
drops below 50% for outer suburbs residents.
Figure 5. Cumulated people according to area of convex hull (2003 OD survey)
5.1.3
Eccentricity measures
The average distance between the home location and the mean centre of the CH
gives a first impression of the eccentricity of the set of activity points with respect to
the home location. As shown in Figure 6, eccentricity varies across population
segments. As could be expected, it is smaller for children and elderly people. Hence,
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features such as living in outer suburbs, owning a car or being a worker increases this
measure. Average eccentricity measure varies from 2.203 in the CBD to 6.530 in the
outer suburbs. Again, not owning a car seems to structure activity locations around
the home location.
Figure 6. Average distance between home location and mean centre of convex hull for various
population segments (2003)
5.2
5.2.1
Activity spaces of households
Enumeration of activity locations
The enumeration of all the activity locations used by the members of a household is
summarised in Table 4 for both datasets. As was observed for individuals, the
average number of locations used during a typical weekday has decreased from 1998
to 2003. Although the average household size did concomitantly decrease during that
same period, segmented estimations by household size confirm the decrease in the
average number of visited locations for every specific size considered.
Table 4. Average number of locations visited by an HOUSEHOLD during a typical weekday
(1998 and 2003)
Figure 7 shows the average number of different locations visited by weekday by the
households according to their size and car ownership. Grey histogram and right site
y-axis further provide the weighted number of households within each segment. The
two datasets confirm the importance of car ownership for location choice since it
increases the number of visited locations notwithstanding the household size.
Actually, in average, an additional car will result in 0.29, 0.26, 0.25 and 0.31 new
activity location by weekday for households of 1, 2, 3 and 4 people and more
respectively.
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Figure 7. Average number of locations visited by weekday (1998 and 2003) and number of
households (2003) according to size and car ownership (2003)
5.2.2
Features of convex hulls
Households’ convex hulls were estimated using a sample of 36 753 households
(929 700 weighed households) and 43 664 households (912 300 weighed
households) for 2003 and 1998 OD surveys respectively. Table 5 presents summary
results regarding the size of convex hulls for various types of households.
Differences between household segments are straightforward: activity spaces (area
and perimeter) increase as household size and car ownership increase and as home
location gets further into suburban areas. However, the key observation is certainly
the fact that all activity spaces increase during the 5 year period even if the number
of locations has decreased during the same period. This is a quite a relevant finding;
actually although the average number of locations visited by a given household
decreases between 1998 and 2003, the size of the activity space increases, denoting a
dispersion in activity locations away from home.
Table 5. Summary features of the convex hulls representing activity spaces of HOUSEHOLDS
during a typical weekday (1998 and 2003)
Figure 8 presents the cumulated proportion of households (of various segments)
according to the area of the convex hull (for the 2003 survey). This graph is
particularly interesting in showing how much convex hull sizes do vary within a
category. For instance, almost 70 % of households owning 2 cars or more have areas
of convex hulls larger than 10 square kilometres while only 20% of those not owning
any car. The graph also clearly illustrates the effects of household size and relative
home location. Considering the fact that household sizes and location are not
independent, part of the differences is due to the fact that households are larger
13
further away from downtown. However, areas, perimeters and CI vary considerably
within household size segments according to their home location. For a two-person
household, the average area varies from 19.6 to 25.5 km2, the perimeter from 23.6 to
44.9 km and the CI from 1.49 to 1.70 depending on whether home is located in the
Montreal Island or in the Outer suburbs respectively.
Figure 8. Cumulated households according to area of convex hull (2003 OD survey)
5.2.3
Eccentricity measures
The study of households’ activity spaces is concluded by the examination of
eccentricity with respect to home location. The following figure (Figure 9) gives
average distances between home location and mean centre of convex hull for various
population segments. This measure of eccentricity is quite stable within households
of two people and more. It however varies considerably according to home location
and car ownership. For instance, every additional car will add 1.1 km to this
eccentricity for 2 people households living in the near suburbs and more than 2.1 km
for the ones living in outer suburbs.
Figure 9. Average distance between home location and mean centre of convex hull for various
household segments (2003)
6.
Discussion and concluding remarks
This paper has demonstrated the capacity of large-scale household travel surveys to
describe the activity spaces of individuals and households in the GMA for two
different time frames, using quantitative measures relating to the convex hull of the
activity locations, and eccentricity in regard to place of residence. Here are some of
the findings and conclusions.
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6.1
Individual level
We observe that activity spaces are very variable, according to gender, car ownership
and occupation. Furthermore, great variations do exist within each sub-category, and
average values do not reveal this variation. Also, differences in Convex Hull size
between groups are much more important than differences in the number of visited
locations between groups. Although the differences in the number of destinations are
small between segments, the differences in the activity space (area, perimeter) are
important.
Between 1998 and 2003, the number of visited activity locations has decreased for
every identified segment and within each age category. Gender differences have
reduced for almost all age categories, in certain cases, differences have even been
inverted (in the 65 to 74 year old category, women visit more locations than men in
2003, the inverse being true in 1998). A previous study has shown that the number of
trips per day has decreased but that the average duration of constrained activities has
increased (duration of work and study activities) (MADITUC, 2004). The hypothesis
that is currently being investigated is that unconstrained activities such as shopping
and leisure are moving to weekends since weekdays are getting busier with work and
study activities.
During the same 1998-2003 period, size of activity areas has however increased
within each subgroup. The five year period shows a decrease for all identified
subgroups and between men and women for nearly all age groups in the number of
visited activity locations. Whereas both the number of visited activity locations has
decreased in every identified subgroups and gender differences have shrunken,
concomitantly, the size of the activity space, as measured by the convex hull area and
perimeter have increased for all subgroups.
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It seems more difficult to comment on the changes between 1998 and 2003 in the
shape of activity spaces. Globally, the compactness index has slightly decreased
(from 1.582 to 1.565), but in certain subgroups, it has slightly increased (students,
near suburbs). Further measures are needed to comment more precisely on the
changes in shape of activity space.
6.2
Household level
Most of the individual-level comments could apply ate the household level. It is quite
obvious that the greater the household, the bigger the convex hull, or the greater the
number of destinations. However, car access within the household, when controlling
for household size, seems to be an important determinant in space use. Similarly,
home location is strongly related to the size of the convex hull for a fixed household
size: the further the home location from the CBD, the larger and less compact the
space used for out-of-home activities.
6.3
Work in progress
Work on the measure of activity spaces using large scale Origin-Destination surveys
is ongoing. The following themes are under examination:

Estimation of other indicators namely the shortest string required to link all
the activity points and the spatial distribution measures available in spatial
statistic software. Hence, the modelling of activity in space and time will be
examined using both individual and household characteristics, as well as
environmental measures around place of residence.

In order to enhance the information available from such datasets,
methodological developments are aimed at constructing typical weeks of
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travel (Mondays vs Tuesdays, etc.) using the day of travel to construct
independent survey samples. Activity spaces will be compared for those five
types of days. Moreover, data mining techniques will be used to identify
groups of population and household presenting similar features and to
construct synthetic people to whom 5-days spatio-temporal behaviours will
be assigned.
6.4
Conclusion
The measure of activity spaces is a promising avenue to better assess the location
choice process by people and households. Large-scale Origin-Destination survey
data have the capacity to reveal various aspects of the daily use of space to perform
out-of-home activities. Although these cross-sectional surveys provide information
on a single day only, the important sample size makes it possible to observe the
typical behaviour of people during a weekday. Surely, multi-day surveys such as
Mobidrive (Schlich and Axhausen, 2003) are complementary, as they specifically
measure the activity space of people over a longer period of time. Consecutive
burden on the respondent however constraints these studies to small sample sizes. In
our case, methodological difficulties related to the big sample size point to the need
for developing new tools to facilitate the measurement of various indicators.
Refining measures and analyses of activity spaces will help predict the consequences
of observable trends (household size, increasing participation of women to the
workforce) and policies (home location vs activity spaces and urban sprawl
phenomena) on the structure of daily behaviours in space and time.
17
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Models, presented at the 10th International Conference on Travel Behavior Research,
Lucerne, Switzerland.
Wets, Geert, Vanhoof, Koen, Arentze, Theo, Timmermans, Harry (2000) Identifying Decision
Structures Underlying Activity Patterns An Exploration of Data Mining Algorithms,
Transportation Research Record 1718, TRB, Washington D.C., pp.1-9
20
Tables
Table 1
Sampling rate
Households
% active households
People
% active people
Trips
1998
4.64%
65,200
91.0%
164,100
79.2%
383,800
2003
3.83%
58,000
90.10%
173,300
79.7%
326,500
21
Table 2
Unit:
PERSON
Average number of locations visited by weekday
1998
2003
People
2.48
2.40
Gender
Men
2.45
2.37
Women
2.51
2.42
Main occupation
Worker
2.59
2.46
Student
2.31
2.23
Car ownership
Yes
2.60
2.49
No
2.35
2.27
Home location
Montreal Island
2.52
2.43
Near suburbs
2.49
2.40
Outer Suburbs
2.41
2.34
22
Table 3
Area
People
17.39
Men
Women
20.17
14.62
Worker
Student
22.58
9.74
Yes
No
22.38
8.55
Montreal Island
Near suburbs
Outer Suburbs
11.55
17.35
28.50
Features of convex hulls
1998
Perim
Comp.Ind. Area
23.56
1.582
Gender
25.34
1.580
21.78
1.595
Main occupation
28.15
1.659
17.50
1.570
Car ownership
27.64
1.636
16.33
1.564
Home location
18.76
1.546
23.07
1.551
33.02
1.732
23
2003
Perim
Comp.Ind.
19.50
24.68
1.565
23.05
16.07
26.78
22.64
1.562
1.582
24.37
10.36
28.98
18.21
1.644
1.584
24.60
9.15
28.72
16.48
1.621
1.526
12.27
17.53
33.52
19.10
23.77
35.09
1.527
1.590
1.697
Table 4
Unit:
HOUSEHOLD
Average number of locations visited by weekday
1998
2003
Households
3.27
3.05
Size
1 person
2.10
1.92
2 people
2.94
2.80
3 people
3.87
3.74
4 people +
4.82
4.72
Car ownership
No car
2.17
2.07
1 car
3.16
2.87
2 cars
4.07
3.76
3 cars +
4.67
4.50
Home location
Montreal Island
3.11
2.94
Near suburbs
3.46
3.17
Outer Suburbs
3.46
3.19
24
Table 5
Unit:
Area
Households
35.63
1 person
2 people
3 people
4 people +
15.90
28.51
38.77
50.43
no car
1 car
2 cars +
7.93
23.95
56.68
Montreal Island
Near suburbs
Outer Suburbs
21.58
35.64
61.37
HOUSEHOLD
Features of convex hulls
1998
Perim
Comp.Ind. Area
31.22
1.46
Size
20.29
1.42
29.27
1.53
33.54
1.51
37.08
1.46
Car ownership
14.96
1.49
26.89
1.54
40.93
1.52
Home location
23.73
1.43
30.61
1.44
45.37
1.62
25
2003
Perim
Comp.Ind.
38.60
32.74
1.48
16.47
31.10
42.49
54.81
21.19
30.66
35.11
38.85
1.46
1.54
1.51
1.47
8.31
24.67
59.18
15.24
27.34
42.25
1.48
1.54
1.54
22.38
35.85
67.23
24.29
31.04
47.83
1.44
1.45
1.63
List of illustrations
Figure 1. Chronology of the spatial location of mobile population during an average
weekday (1998) – square kilometre cells (Morency and Chapleau, 2004) .................. 7
Figure 2. Schematic illustration of the activity spaces of people and households ....... 9
Figure 3. Average number of locations visited by weekday according to age and
gender (1998 and 2003) ............................................................................................. 10
Figure 4. Frequency distribution of the number of activity locations visited on a
average weekday for various population segments (2003 OD survey)..................... 10
Figure 5. Cumulated people according to area of convex hull (2003 OD survey) .... 11
Figure 6. Average distance between home location and mean centre of convex hull
for various population segments (2003) ..................................................................... 12
Figure 7. Average number of locations visited by weekday (1998 and 2003) and
number of households (2003) according to size and car ownership (2003) .............. 13
Figure 8. Cumulated households according to area of convex hull (2003 OD survey)
.................................................................................................................................... 14
Figure 9. Average distance between home location and mean centre of convex hull
for various household segments (2003) ..................................................................... 14
26
5h00
9h00
12h00
15h00
18h00
20h00
27
CH P1
xCH P , yCH P
1
1
Hi
CH H i
Hi
xCH Hi , yCH Hi
CH P2
xH i , y H i
xH i , y H i
xCH P , yCH P
2
Activity spaces of people
2
Activity spaces of households
28
29
Men-98
Women-98
Men-03
Women-03
75 y.o.+
70-74 y.o.
65-69 y.o.
60-64 y.o.
2.1
55-59 y.o.
2.2
50-54 y.o.
45-49 y.o.
40-44 y.o.
35-39 y.o.
30-34 y.o.
25-29 y.o.
20-24 y.o.
15-19 y.o.
10-14 y.o.
5-9 y.o
Average number of locations visited by weekday according to
gender and age
2.8
2.7
2.6
2.5
2.4
2.3
2.0
Distribution of people according to the number of locations
visited by weekday
90%
% people in segment
80%
Car ownership: Yes
70%
Car ownership: No
60%
Main occupation: Worker
50%
Main occupation: Student
40%
30%
20%
10%
0%
1
2
3
4
5
6
Number of locations visited by weekday
30
7
8+
Cumulated people according to area of convex hull (2003)
100%
90%
80%
70%
60%
Men
Women
50%
Owns a car
No car
40%
Worker
30%
Student
Montreal Island
20%
Near suburbs
10%
Outer Suburbs
0%
0
5
10
15
20
25
30
Area of convex hull (km 2)
31
35
40
45
50
Average distance between Home location and Mean center of Convex hull
for various population segments (2003)
9.0
Home - Convex Hull distance (km)
8.0
Outer Suburbs
7.0
Ow ns a car
6.0
5.0
Worker
Near suburbs
4.0
3.0
No car
Montreal Island
2.0
1.0
0.0
5-9
y.o
10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75
y.o. y.o. y.o. y.o. y.o. y.o. y.o. y.o. y.o. y.o. y.o. y.o. y.o. y.o.+
32
Average number of locations visited by weekday by
households according to size and car ownership
2003
5
250,000
1998
1 pers.
2 people
3 people
33
4 people +
3 cars +
2 cars
1 car
0 car
3 cars +
2 cars
0
1 car
0
0 car
50,000
3 cars +
1
2 cars
100,000
1 car
2
0 car
150,000
3 cars +
3
2 cars
200,000
1 car
4
Number of households (2003)
300,000
HH 2003
0 car
Average number of locations
6
Cumulated households according to size of convex hull (2003)
100%
90%
80%
70%
60%
Montreal Island
50%
Near suburbs
40%
Outer suburbs
30%
1 person
4 people +
20%
no car
10%
2 cars +
0%
0
10
20
30
40
50
2
Area of convex hull (km )
34
60
70
Average distance between home location and mean center of
convex hull for various household segments (2003)
Montreal Island
Near suburbs
12.0
Outer Suburbs
10.0
8.0
6.0
4.0
2.0
1 pers.
2 people
3 people
35
4 people +
3 cars +
2 cars
1 car
0 car
3 cars +
2 cars
1 car
0 car
3 cars +
2 cars
1 car
0 car
3 cars +
2 cars
1 car
0.0
0 car
Home-Convex Hull distance (km)
14.0