Estimation of Transient Occupants on Weekdays and Weekends for Risk Exposure Analysis

Osaragi Risk Exposure Analysis for Weekdays and Weekends

Toshihiro Osaragi Tokyo Institute of Technology [email protected]

ABSTRACT

Understanding the characteristics of the spatiotemporal distribution of a population, which varies according to the time of the day and the day of the week (weekday or weekend), is one of the most important issues in the field of urban disaster mitigation planning. However, the existing Person Trip Survey data based on weekdays is not appropriate for estimating the spatiotemporal distribution of population on weekends. In the present study, we proposed a method for converting existing Person Trip Survey data from a weekday base to a weekend base and examined the differences in the spatiotemporal distribution of the population. Using these databases, we attempted to compare the number of deaths due to building collapse estimated for weekdays and weekends for various districts in the Tokyo Metropolitan area.

Keywords Risk exposure, weekday/weekend, spatiotemporal distribution, person trip survey, collapsed building.

INTRODUCTION It is vital to have an accurate grasp of the constantly varying spatiotemporal distribution of transient occupants of the metropolitan region, including their ages, reasons for travel and other variables, in order to draw up detailed plans for disaster mitigation (Fukushima and Nakano, 1999). Until now, however, estimates of human casualties have relied on the static distribution of population provided by the national census and other counts (Osaragi and Hoshino, 2012). Thus, many recent-approved studies are based on a seismic intensity map, and only consider resident population from the census data (Smith, Martin and Cockings, 2014; Martin, Cockings and Leung, 2015). The actual spatial distribution of residents on the move in any busy metropolitan area changes by the hour, or even by the minute, since a large number of people are travelling for a variety of purposes over long distances, using rapid-transit or other rail facilities (Osaragi, 2013). Freire , Florczyk and Ferri (2015) suggested that there is a need for population distribution data that goes beyond a residence-based (i.e. nighttime) representation, since hazard events can occur at any time and with little forewarning. Actually, they demonstrated that there are significant differences in potential exposure from night- to daytime in all hazard levels, though the case study on Campi Flegrei. Smith et al. (2014) and Smith, Newing, Quinn, Martin, Cockings and Neal (2015) focused on the coupling of population and environmental models to address the effect of seasonally varying populations on exposure to flood risk, and demonstrated that large fluctuations occur over time in the population distribution within flood risk zones. Martin et al. (2015) identified the importance of assembling an underlying data model at the highest resolution in each of the spatial, temporal, and attribute domains, after reviewing attempts to construct time-specific representations of population. Accurately estimating population exposure is a key component of catastrophe loss modeling, one

Long Paper – Geospatial Data and Geographical Information Science Proceedings of the ISCRAM 2016 Conference – Rio de Janeiro, Brazil, May 2016 Tapia, Antunes, Bañuls, Moore and Porto de Albuquerque, eds.

Osaragi Risk Exposure Analysis for Weekdays and Weekends

element of effective risk analysis and emergency management, and it requires moving beyond using simple residence-based census data (Bhaduri, Bright, Coleman and Urban, 2007; Fielding 2007; Aubrecht, Özceylan, Steinnocher and Freire, 2012; Freire and Aubrecht, 2010). The 1995 Southern Hyogo Prefecture Earthquake (commonly known as the Kobe Earthquake) took place in the early morning, when most residents were still at home; had it struck during the day, the scale of damage would have been vastly different. For detailed analysis such as modeling of hazards and risk exposure there is a need for even more detailed and high resolution population distribution (Bhaduri et al. 2007; Aubrecht, Steinnocher, Hollaus and Wagner, 2009; Freire, 2010; Freire, Aubrecht and Wegscheider, 2011; Freire et al., 2015). Given this background, Osaragi and his research group have developed a model for predicting the spatiotemporal distribution of transient occupants and passengers in a city, with the object of preparing basic data for regional disaster planners. Person Trip Survey data (PT data) for individuals in the Tokyo Metropolitan area can be input to the model to obtain the spatiotemporal distributions of different classifications of transient occupants (the details of PT data are described later). These can also be combined with the information on facilities contained in GIS data (scale and spatial distribution of buildings, etc.) to construct a model for estimating more detailed spatiotemporal distribution of the populations inside buildings (Osaragi and Hoshino, 2012). Other models have been constructed to estimate the amount of people using railways (Osaragi and Otani, 2009) and automobiles (Osaragi, 2015). Since the PT data making up the original data are based on normal working day (weekday) traffic, however, they are of little use for estimating numbers of transient occupants in Tokyo on weekends or other days off work (bank holidays). Tsuji’s primary study (Tsuji, 1982) on transient occupants on weekends used population densities by building type, on the basis of population densities in buildings and commercial statistical surveys, to establish estimates of transient occupants for various seasons, days of the week, and time of day. However, He could not estimate spatial distributions of transient occupants for the wider metropolitan region, since he had concentrated on the numbers of transient occupants inside buildings. This paper examined methods of establishing PT data for weekends that can be applied as original data in previously established models to predict spatiotemporal distributions of transient occupants in Tokyo. National Time Use Survey (NHK Broadcasting Culture Research Institute, 2000) was examined in order to propose a procedure for converting PT data for weekdays to data for weekends. The accuracy of the estimate was validated in terms of the activities of the various portions of the population and the spatiotemporal distribution of transient occupants. The transient occupants were then analyzed to compare weekdays and weekends with respect to the characteristics of the predicted populations. The numbers of casualties in Setagaya Ward, Tokyo, due to building collapse subsequent to a large earthquake was also estimated, and the numbers on weekdays and weekends were compared.

METHOD FOR CREATING PT DATA FOR WEEKENDS

Outline of PT data and NHK Time Use Survey data Person Trip Survey aims at studying the actual travel behavior in cities of Japan, and has been carried out every ten years by the Ministry of Land, Infrastructure, Transport and Tourism of Japan. "Trips" defined in PT data are illustrated in Figure 1, as an example.

Figure 1. Example of trips contained in Person Trip Survey data

Osaragi Risk Exposure Analysis for Weekdays and Weekends

Some of the information provided in PT data is listed in Table 1. They include personal attributes (age, gender, occupation), the position and time information of departure and arrival, purpose of trip (18 purposes: e.g., commuting, business, shopping, eating), and means of transportation (5 means: on foot, bicycle, car, bus, train, ship, airplane), etc. The area for this survey is shown in Figure 2. It covers a range of 70 km radius centered on the Tokyo. About 1.2 million persons were selected from around 33 million residents by random sampling based on census data. The number of valid samples is 883,044. Item Contents Regions subject to survey Tokyo, Kanagawa, Saitama, Chiba and Southern Ibaragi Prefectures Survey time and day 24 hours on weekdays in October of 1998, excluding Monday and Friday Object of survey Persons aged 5+ living in the above region Random sampling based on census data (1,235,883 persons selected from Sampling 32,896,705 persons) Valid data 883,044 samples (mean weighting coefficient is approximately 37.3) Content of data Personal attributes, position and time of departure/ arrival, purpose of trip, etc. Purpose of trip Purpose of each trip (18 purposes : e.g., commuting, business, shopping, eating) Means of trip Means of trip (5 means: on foot, bicycle, car, bus, train, ship, airplane)

Table 1. Outline of Person Trip Survey data

Tokyo Setagaya 10km 20km 30km Tokyo The Pacific Bay 40km Ocean 50km 60km Sagami 70km Bay

Figure 2. Study area and boundaries of survey unit (zone) for Person Trip Survey, and location of Setagaya Ward

Osaragi Risk Exposure Analysis for Weekdays and Weekends

HHK (Nippon Hoso Kyokai in Japanese) is the public service broadcaster in Japan. NHK Broadcasting Culture Research Institute conducts Time Use Survey every five years since 1960. This survey captures the one day life of people from the viewpoint of the time, and provides basic data which are established to reveal Japanese living conditions, and investigates with home life and their behavior for 2 days (for each 15-minute from 0:00 to 24:00), which include not only weekdays but also weekends. At the same time, the personal attributes (gender, age, occupation) of responders are also included. Time Use Survey data used in this research is based on the survey conducted on October, 2000, and 45,120 people were extracted by random from the basic resident register. The number of effective sample was 32,984 (73.1%).

Comparison of PT data with Time Use Survey data The fraction of a population engaged in a certain activity at any given time of day is a key parameter for converting data. PT data and Time Use Survey data are gathered by different methods from different groups, so we began by comparing the numbers of people involved in various activities by time of day (hourly activity fractions) obtained by the two data sets (data from weekdays). The hourly activity fractions (7 activity types given in Table 2) were estimated from the travel purposes and destination building types reported by the survey subjects in PT data, and were compared for commonality with Time Use Survey results. The comparison is shown in the left column of Figure 3. The data sets match each other in terms of the hour activity fractions. Since meal times cannot be distinguished from other activities in PT data, the model shows little commonality between the groups at home, at work or at school in the middle of the day. Activity types Definition of activities 1 Commuting to work Engaged in work (including traveling to workplace and working) 2 Attending school Learning at school (including traveling to school and learning) 3 Shopping (short time) Shopping (less than 120 minutes) 4 Shopping (long time) Shopping (120 minutes or more) 5 Going out on private (short time) Leisure and all going-out activities other than working, schooling, shopping (less than 120 minutes) 6 Going out on private (long time) Leisure and all going-out activities other than working, schooling, shopping (120 minutes or more) 7 Staying at home Staying at home all day Note: People who are doing multiple activities are classified by priority in the order of Commuting to work, Attending school, Shopping, Going out on private. Also, people who are Shopping and Going out on private are classified into the longer time activities.

Table 2. Activity types in daily life

Osaragi Risk Exposure Analysis for Weekdays and Weekends

Figure 3. Temporal profiles at an hourly resolution for selected activity types for weekdays and weekends

Osaragi Risk Exposure Analysis for Weekdays and Weekends

Method for handling PT data for weekends The weekend PT data were created with the assumption that an individual’s weekend travel can be modeled with (described by) another individual’s travel on a weekday. The author selected two subjects who fit into the same classification (sex, age, profession, etc.) and uses the trip data for one subject on a weekday to create the weekend PT data for the other subject. Each of the subjects was classified into six attributes (Table 3) and his or her trip data were examined to extract the activity types in daily life (Table 2). Table 4 shows the fractions of activity types for each attribute classification on weekdays. Attributes of people 1 Employee (work on weekdays) 2 Employee (other activities on weekdays) 3 Schoolchild (aged twelve or less) 4 Student (aged more than thirteen) 5 Housewife/ househusband 6 Unemployed

Table 3. Attribute classification of people

Weekday

Ratio of Com- Attending Shopping Shopping Going Going Staying Attributes of activity muting school (short (long out on out on at home people types to work time) time) private private (short (long time) time) Employee 0.529 0.839 0.000 0.019 0.011 0.013 0.038 0.080 Schoolchild 0.108 0.001 0.963 0.002 0.002 0.003 0.013 0.017 (aged twelve or less) Student (aged 0.079 0.014 0.851 0.008 0.009 0.005 0.041 0.072 more than thirteen) Housewife/ 0.154 0.032 0.000 0.267 0.102 0.115 0.257 0.227 househusband Unemployed 0.130 0.030 0.000 0.124 0.061 0.085 0.220 0.481

Table 4. Fractions of activity types for each attribute classification on weekdays

The hourly activity fraction on weekends is described by using the activity type fraction of each attribute- classification on weekends, ajl (fraction of activity type l of attribute classification j on weekends), as follows: qit(, ) Qit(, )= , (1) ∑qit(, ) i qit(, ) pkk (, ita ) n , (2) = ∑∑∑ jl jl j kjl

Osaragi Risk Exposure Analysis for Weekdays and Weekends

where Q(i, t): Estimated fraction of persons engaged in activity i at time t on weekends q(i, t): Number of persons engaged in activity i at time t on weekends k n j: Number of persons of classification j in zone k k p jl(i, t): Fraction of persons of classification j in activity type l in zone k engaging in activity i at time t. pk (, i t ) 1 ( for all j , k ,, l t ) ∑ jl = i k k The values of n j and p jl(i, t) are estimated from PT data. The value of unknown parameters ajl is found by seeking the minimum of the statistical quantity f(a) in the following equation (a is vector expression of unknown parameters ajl):

2 * , (3) fQitQit()a =∑∑⎣⎦⎡⎤ (,)− (,) it where Q*(i, t): Measured values for fraction of persons engaged in activity i at time t on weekends, obtained from Time Use Survey data. Then, we must satisfy the following constraints on the parameters: gjl()a =− a jl < 0( for all j ,) l , (4) ha()a 1 0( forallj ). (5) jjl=∑ −= l gjl(a) is an inequality constraint function and hj(a) is an equality constraint function. This is a constrained nonlinear optimization problem (ASNOP Study Group, 1991), here solved using the exterior penalty method to achieve convergence for ajl. The calculation is managed to drive the exterior penalty function P(a, r) toward zero:

⎡⎤2 Pr(,)aa f () r maxg (),0 a h () a2 , (6) =+⎢⎥∑∑{ jl} + ∑ i ⎣⎦jl j where r is the penalty parameter, a positive number increasing as the number of convergence iterations increases.

The essential steps of the workflow to estimate the unknown parameters ajl and weekend PT data is illustrated in Figure 4.

(1) Set the unknown parameters ajl at random. (2) Calculate the number of individuals (A) who spent weekends in activity type l. This value was estimated by k multiplying the parameters ajl by the number of individuals in zone k of classification j, n j, obtained from weekday PT data. (3) Extract individuals (B) who spent weekdays in activity type l from the same attribute classification j in zone k at random. (4) Substitute trips of individuals (A) on weekends by trips of individuals (B) on weekdays, and estimate weekend PT data. (5) Check the consistency of hourly activity fractions estimated from weekend PT data and weekend Time Use Survey data.

(6) If the differences are not acceptable, update the unknown parameters ajl, and return to the process (2). (7) Otherwise (the differences are small enough) this iteration is stopped and we complete the estimation of weekend PT data.

Osaragi Risk Exposure Analysis for Weekdays and Weekends

(1) Set the unknown parameters ajl at random Check the consistency of hourly activity fractions extracted from (A) and (B) (see Figure 3, left panel)

(A) PT data (weekday) (B) Time Use Survey data (weekday)

change parameters Parameters : ajl Fraction of activity type l of attribute classification j on weekend: ajl k The number of individuals in zone k of attribute classification j : nj (2) Calculate the number of individuals (A) who spent weekend in activity type l in zone k : k x nj ajl (3) Extract individuals (B) who spent weekday in activity type l from the same attribute classification j in zone k at random

(4) Substitute trips of individuals (A) on weekend Iteration for by trips of individuals (B) on weekday minimization

(5) Check the consistency of hourly activity fractions extracted from (C) and (D) (see Figure 3, right panel) No (6) Differences in hourly activity fraction is small enough?

Yes

(7) Estimation of weekday PT data into weekend PT data is completed

Figure 4. Diagram of the essential steps of the workflow for estimating weekend PT data

Table 5 shows the estimated ajl for weekends. The estimated ajl was used to select out individuals of the same attribute classification from a single zone at random, and their trips made during weekdays were used for describing the trips of the same classification persons on weekends, and the weekend PT data were created. Weekend Com- Attending Shopping Shopping Going Going Staying Attributes of people muting to school (short (long out on out on at home work time) time) private private (short (long time) time)

Employee (work on 0.120 0.006 0.147 0.161 0.089 0.349 0.128 weekday) Employee (not 0.839 0.000 0.020 0.012 0.010 0.040 0.079 work on weekday) Schoolchild (aged 0.005 0.078 0.163 0.122 0.108 0.379 0.146 twelve or less) Student (aged more 0.165 0.081 0.131 0.093 0.086 0.288 0.156 than thirteen) Housewife/ 0.000 0.000 0.268 0.116 0.118 0.274 0.223 househusband Unemployed 0.000 0.000 0.129 0.073 0.089 0.231 0.477

Table 5. Estimated fractions of activity types for each attribute classification on weekends

Osaragi Risk Exposure Analysis for Weekdays and Weekends

ACCURACY OF ESTIMATES FOR WEEKEND TRIPS

Accuracy of estimated fractions of the population The fractions of the population engaged in activity types during periods of the day on weekends were calculated from the weekend PT data in order to validate the accuracy of the above estimates. These results were compared with the findings of Time Use Survey (Figure 1, right column). These showed approximately the same consistency with all activities as the data from weekdays.

Accuracy of PT spatial distributions on weekends There are no data available for spatial distributions of transient occupants of Tokyo on weekends. Therefore, the present study examined railway users. First, the ratio of the spatial distribution on weekdays to that on weekends (i.e., the ratio of passengers on weekends to passengers on weekdays) was calculated. Figure 5 shows this ratio for central Tokyo. The weekend: weekday ratio is high in Shibuya, Shinjuku and other locations with large numbers of commercial facilities but low in Otemachi, Kudanshita and other locations with large numbers of office buildings. These results fit our assumptions of weekend events.

Ikebukuro

Ueno

Kudanshita Shinjuku Otemachi Tokyo Yurakucho 0.0 - 0.1 0.1 - 0.2 0.2 - 0.4 Shibuya 0.4 - 0.6 0.6 - 0.8 0.8 - 1.0 Shinagawa 1.0 - 1.2 1.2 - 1.4 Osaki 1.4 - 1.6 Fraction of 1.6 - 1.8 weekend/ weekday 1.8 -

Figure 5. Spatial distribution of the weekend: weekday ratio of the number of passengers at each station Next, weekday and weekend train passenger count data on a railway company by time of day (per hour) were examined for comparison with the above findings. The figures are from three stations in central Tokyo, in a suburb, and between the suburb and downtown. Figure 6 (left column) provides the results. There was an approximately one hour discrepancy between the actually observed data and the PT data for train passengers in the evening peak on weekdays, but the reported times for the morning peak matched exactly, and the mean absolute error between the two was minor at 30%. Next, the estimated weekend PT data were examined to confirm their accuracy for numbers of railroad passengers by time of day (Figure 6, right column). The distribution for the weekends showed none of the sharp peaks seen in the weekday data; rather, the changes were more gradual. These estimates expressed the trends in the actually observed numbers quite well. The estimates for a station in downtown Tokyo offered rather low accuracy, but the mean uncertainty remained within 30%; these estimates show an equivalent consistency as the direct PT data weekday estimates.

Osaragi Risk Exposure Analysis for Weekdays and Weekends

Weekday Weekend Station A R2=0.87 R2=0.80 (downtown Tokyo) 6000 6000

4000 4000

2000 2000

0 3 6 9 12 15 18 21 0 (O’clock) 0 3 6 9 12 15 18 21 0 (O’clock)

Station B R2=0.80 R2=0.83 (between suburb 6000 6000 and downtown) 4000 4000

2000 2000

0 3 6 9 12 15 18 21 0 (O’clock) 0 3 6 9 12 15 18 21 0 (O’clock)

Station C R2=0.90 R2=0.90 (suburb) 6000 6000 estimated estimated 4000 observed 4000 observed

2000 2000

0 3 6 9 12 15 18 21 0 (O’clock) 0 3 6 9 12 15 18 21 0 (O’clock)

Figure 6. Comparison of the number of passengers in weekdays and weekends for each time period and each station

Osaragi Risk Exposure Analysis for Weekdays and Weekends

SPATIOTEMPORAL DISTRIBUTION OF TRANSIENT OCCUPANTS ON WEEKDAYS AND WEEKENDS

Spatiotemporal distribution of transient occupants Figure 7 shows the spatiotemporal distribution of transient occupants who were in offices/banks on weekdays and weekends. These populations were higher on weekdays than on weekends in areas surrounding central Tokyo. This indicates that there would be greater troubles in this area due to building damage on weekdays than on weekends.

Ikebukuro

Shinjuku Weekday Tokyo Shibuya

07:00 12:00 17:00

Ikebukuro

Weekend Shinjuku Tokyo Shibuya

0 3 7.2 15 35 (x 1,000 population) 0 5 km

Figure 7. Spatiotemporal distribution of people in offices and banks on weekdays and weekends

Figure 8 presents the spatiotemporal distribution of transient occupants outside of buildings on weekdays and weekends. The population of transient occupants was lower in the morning (7:00) on weekends, since they are still at home. However, the extent of concentration into central Tokyo was higher at noon on weekends. In central Tokyo, transient occupants are also concentrated in the vicinities of Tokyo Station, Shinjuku, Ikebukuro, Shibuya and other zones with principal train stations. There are prominent differences in numbers of transient occupants between locations due to the scale and number of their commercial buildings. Thus, we confirmed the totally different spatiotemporal distribution of transient occupants on weekdays and weekends.

Osaragi Risk Exposure Analysis for Weekdays and Weekends

Ikebukuro

Weekday Shinjuku Tokyo Shibuya

07:00 12:00 17:00

Ikebukuro

Weekend Shinjuku Tokyo Shibuya

0 3 7.2 15 35 (x 1,000 population) 0 5 km

Figure 8. Spatiotemporal distribution of people outside of buildings on weekdays and weekends

COMPARISON OF CASUALTIES DUE TO BUILDING COLLAPSE ON WEEKDAYS AND WEEKENDS

Model of human casualties Figure 9 compares the rates of total collapse and the resulting death toll by ward (city subsection) and by victim age classification with those events recorded in the official report on the Southern Hyogo Prefecture Earthquake (Itoigawa et al. 1999). The parameters were found to have a linear relationship, allowing formulation of a simple regression model to describe the death rate from the percentage of buildings suffering total collapse (complete destruction). This model is simple but shows high consistency. Once the number of transient occupants and probability of complete building collapse have been estimated, they can be combined to estimate the number of deaths in buildings. The probability of death corresponding to the probability of total collapse is multiplied by the transient occupant population of each district to estimate the death toll.

Age group Age group Age group (%) 5 - 14 years old (%) 15 - 64 years old (%) 65 - years old 0.3 3.0 ) 2 2 2 R =0.897 % R =0.743 R =0.843 ( 0.4 e g

a 2.0 t 0.2 n e c r

e 0.2 y=0.057 x p 1.0 y 0.1 y=0.00112 x t i

l y=0.0070 x a t r o

M 0 0 00 10 20 30 (%) 0 10 20 30 (%) 0 10 20 30 (%) Fraction of complete destruction of buildings in each city (%)

Figure 9. Predicted rates of total collapse and resulting death toll by ward (city subsection)

Pre-processing of data for calculation of casualties The spatiotemporal population data for weekdays and weekends have been produced for the whole of the Tokyo metropolitan area (Figure 2). However we attempt to make exposure analysis for Setagaya Ward in Tokyo (Figure 2, Figure 10), since the detailed data necessary for estimating seismic damage were available. Also Setagaya Ward is a typical residential area, where the number of people varies according to the time of the day

Osaragi Risk Exposure Analysis for Weekdays and Weekends

and the day of the week. This means that we can expect clear results for this area. The exposure analysis demonstrated here can be easily extended to the whole of the Tokyo metropolitan area, if the detailed data will be available. Casualties resulting from the collapse of buildings were estimated for comparison between weekdays and weekends. Detailed GIS data are available for buildings in Setagaya Ward, Tokyo (Setagaya Ward, 2007). This dataset was originally a part of Tokyo City Planning Geographic Information System Data conducted in the Tokyo Metropolitan area in 2006, and it includes some detailed information on the age and structure of buildings. First, the number of transient occupants was estimated for the chōme in Figure 10 (chōme, in the Japanese address system, is approximately equivalent to “block”; below, “the region”). The analytical procedure was as follows. It was assumed that, if the type of building had been determined, the number of transient occupants was proportional to the building’s floor area. The building type recorded in the PT data and the floor area provided in the GIS data were combined to allot the portions of the transient population into each building.

Inogashira line

Keio line

Setagaya line Toyoko line

Odakyu line

Oimachi line Meguro line Den-en toshi line

N 0 1 2 km Ikegami line

Figure 10. Study area for casualties due to building collapse (Setagaya Ward)

Building collapse model It is widely believed that the Tokyo region will be struck by a severe earthquake in the near future. This has spurred appeals to authorities to revise their regional disaster mitigation plans. In the present study, we assumed a severe earthquake, in which epicenter is in the northern region in Tokyo Bay. The scale of the earthquake is magnitude-M7.3, and the maximum seismic intensity is 7 on the Japanese scale. The building collapse model (Murao and Yamazaki, 2001) was adopted here; it predicted the probability of collapse, given the number and ages of buildings (Figure 11). The building structures and ages in Setagaya Ward were input to the model and the collapse probabilities were calculated. Means were calculated throughout the region shown in Figure 10 to find the spatial distribution of affected buildings in terms of the actual addresses. Setagaya Ward has many wooden and aging structures, and a considerable number of these are at risk of complete collapse.

n 1.0 1.0 n o i o t i

wooden t c 0.9 0.9 ～1951 c u u r reinforced concrete

t 1952～1961 r

0.8 t 0.8 s

steel s e 1962～1971 e d 0.7 0.7 light-gauge steel d 1972～1981 e t e t

e 0.6 0.6 1982～ l e l p 0.5 p 0.5 m m o o c

0.4 c 0.4 f f o 0.3 o 0.3 y t y i t l i i l 0.2 i 0.2 b b a a b

0.1 b 0.1 o o r r

P 0.0 0.0 0 20 40 60 80 100 120 140 160 180 P 0 20 40 60 80 100 120 140 160 180 Peak Ground Velocity (cm/s) Peak Ground Velocity (cm/s)

Figure 11. Probability of complete destruction of buildings varying according to building structure and age

Osaragi Risk Exposure Analysis for Weekdays and Weekends

Analysis of casualties due to building collapse The number of transient occupants of a zone containing a university, companies or offices fluctuates widely throughout the day on a weekday. The fluctuations are milder on a weekend. As large numbers of transient occupants leave Setagaya Ward on weekdays, the number of residents is small; this number is greater on weekends. High peak ground velocities increase the death toll due to building collapse on both weekdays and weekends, on mornings or evenings, when the transient occupant population is high. Casualties are also higher on weekends, when people are at home, than on weekdays. Figure 12 (left panel) shows how the populations at home and away from home, and related death rates, vary with the hour of day. Casualties at home are higher on weekends, and casualties away from home are higher on weekdays. Figure 12 (right panel) graphs the casualty rate for different age ranges as it varies with the hour of day. The age range showing the greatest variation between weekdays and weekends in death rate is the 13 y.o. – 64 y.o. population, i.e., the population not requiring additional assistance. Thus, weekdays and weekends show great differences in the expected situation following a disaster and in the age range of the victims. Many people move into the city center on weekdays, so the locations of expected casualties vary with the time of day.

1000 600 s s h h t t a a 500 e 800 e d d f f o o 400 r r

e 600 e b b 300 m m u u n 400 n d d 200 e e t t a 200 a m m 100 i i t t s s

E 0 E 0 7 9 12 15 20 7 9 12 17 20 O'clock O'clock weekday (12 years or less) weekend (12 years or less) weekday (at home) weekend (at home) weekday (13 - 64 years) weekend (13 - 64 years) weekday (in other building) weekend (in other building) weekday (65 years or more) weekend (65 years or more)

Figure 12. Time changes in the estimated number of deaths for each age group on weekdays and weekends

Figure 13 shows how the projected death tolls vary for different districts, assuming a disaster at noon on a weekday and a weekend. Casualties are high on weekdays in zones containing many offices and companies, and are high on weekends in residential zones. Thus, casualties are expected to differ greatly between weekdays and weekends, depending on the land use of the location. d n

e 40 Okuzawa k e e w Shimouma n

o 30 Kitakarasuyama Funabashi Daizawa

s Todoroki h

t Kitazawa

a Seijo e Ikejiri d

f 20 Daizawa

o Funabashi Sakuragaoka r

e Seijo

b Fukasawa m

u 10 Ikejiri

n Sakuragaoka Fukasawa d

e 12 O’clock t a

m 0 i

t 0 10 20 30 40 s

E Estimated number of deaths on weekday

Figure 13. Relationship between the estimated death toll on weekdays and weekends

Osaragi Risk Exposure Analysis for Weekdays and Weekends

Spatiotemporal distribution of people expected to require additional assistance during disasters Transient occupants aged 12 and under and 65 and older were here considered the population identified as requiring additional assistance during disasters. Many of this population live in outside the Japan Railways Yamanote Line (a belt line around Tokyo). Setagaya Ward is one of these locations. We need to pay more attention to those locations if a disaster strikes in daytime, since many commuters are staying in a city center and can't assist those people requiring additional assistance. Commuters going to work (i.e., individuals who don't need additional assistance during disasters) could be expected to assist the most vulnerable passengers on weekdays, as could shoppers and other passengers riding the trains for personal reasons on weekends, so the requirement for additional assistance would generally be lower at those times. However, since many able-bodied individuals travel from the suburbs into downtown Tokyo, the population requiring special assistance would be high at such times. Figure 14 (left panel) compares the fractions of population represented by the population requiring additional assistance during disasters on weekdays and weekends in Setagaya Ward. The zones with large numbers of offices and companies showed low fractions on weekdays (less than 0.2 %), while relatively high fractions were seen on weekdays in residential zones (larger than 0.3 %). The spatiotemporal distribution of the population requiring additional assistance during disasters is also shown in Figure 14 (right panel). Numbers were low on weekdays in zones with offices, companies and universities, but somewhat high in residential districts. g n i r i 0.40 u

q Sakurashinmachi Miyasaka e

r 0.35 Gotokuji Kitazawa s （ s Komazawa Park) Sakura Hanegi a d 0.30 Tamagawadai n d Miyasaka e e Yoga Hanegi Daita i k f i t e 0.25 Tamagawa Soshigaya Soshigaya n e Gotokuji e w Daita d

i 0.20 n Shimouma

s o （Kinuta Park) Kitazawa

n Kaminoge e 0.15 Yoga o

c Kitami Shimouma s n r Kitami a e Okamoto Sakurashinmachi t 0.10 p s i f s

o Okamoto s 0.05 e a l g 12 O’clock a a

t Tamagawa

n 0.00 n

o Kaminoge i e 0.00 0.10 0.20 0.30 0.40 t i c r d

e Percentage of persons identified as requiring d P a additional assistance on weekdays

Figure 14. Relationship between percentage of population requiring additional assistance on weekdays and weekends

The variation of the fraction of transient occupants inside collapsed buildings with the fraction of the population requiring additional assistance during disasters was estimated, and their spatial distribution was shown in Figure 15. Some residential districts (colored by purple) showed high ratios of both groups on both weekdays and weekends. Since large numbers also left these districts to go to work, however, this depleted the numbers of vigorous younger individuals like students who are able to help the population requiring additional assistance in evacuation. Thus, it may be more difficult to carry out timely rescue activities immediately after an earthquake on a weekday than on a weekend. This suggests that emergency planners for residential areas should consider performing drills assuming emergencies during the day on weekdays, because these locations would have the highest ratio of population requiring additional assistance during disasters to be rescued during those periods.

Osaragi Risk Exposure Analysis for Weekdays and Weekends

Sakurajosui Kyuden Daizawa Miyasaka

Soshigaya Gotokuji Kyodo Sangenchaya Regional disaster prevention is vulnerable on weekday Nakamachi on hoiday on weekday and weekend

Higashitamagawa

Figure 15. Vulnerable regions for population requiring additional assistance during disasters on weekdays and weekends

SUMMARY AND CONCLUSIONS This paper proposed a method for converting PT data from weekdays to PT data on weekends using information from Time Use Survey. It also compared spatiotemporal distributions of passengers at Tokyo railroad stations on weekdays and weekends and validated these figures. The weekend PT data created in this method provide an estimate of the spatiotemporal distribution of the transient occupants in Tokyo, which differs greatly from that on weekdays, and also provide crucial information about this population. Combining these with detailed data on buildings allows estimation of the number of victims of a disaster due to the collapse of buildings. The casualties in locations such as Setagaya Ward that contain many residential districts would be larger on weekends. However, since the portion of population represented by individuals requiring additional assistance during disasters is high during the day on weekdays, the risk in terms of rescue activities after a disaster is higher on weekdays. Thus, it is appropriate to take the details of individuals’ status (working, student, age, etc.) into account when estimating the number of casualties due to a disaster. The fund of information constructed in this study about the transient occupants in Tokyo will prove to be useful as basic data for disaster managers.

ACKNOWLEDGMENTS A portion of this paper was published in the Journal of Architectural Planning and Engineering (Architectural Institute of Japan), in an article entitled “Spatio-Temporal Distribution of Population on Weekdays and Holidays for Human-damage Estimation of Devastating Earthquakes”, 635, pp. 145-152, 2007 (in Japanese). The author would like to give his special thanks to Mr. Ren Shimada for computer-based numerical calculations.

REFERENCES 1. ASNOP Study Group (1991) Nonlinear optimization programming: PC FORTRAN version, Nikkan Kogyo Shimbun, LTD. 2. Aubrecht, C., Steinnocher, K., Hollaus, M, and Wagner, W. (2009) Integrating earth observation and GIScience for high resolution spatial and functional modeling of urban land use, Computers, Environment and Urban Systems 33:15-25. 3. Aubrecht, C., Özceylan, D., Steinnocher, K. and Freire, S. (2012) Multi-level geospatial modeling of human exposure patterns and vulnerability indicators. Natural Hazards, 1–17. 4. Bhaduri, B., Bright, E., Coleman, P. and Urban, M. (2007) LandScan USA: a high-resolution geospatial and temporal modeling approach for population distribution and dynamics. GeoJournal, 69 (1), 103 – 117. 5. Fielding, J. (2007) Environmental injustice or just the Lie of the land: an investigation of the socio-economic Long Paper – Geospatial Data and Geographical Information Science Proceedings of the ISCRAM 2016 Conference – Rio de Janeiro, Brazil, May 2016 Tapia, Antunes, Bañuls, Moore and Porto de Albuquerque, eds.

Osaragi Risk Exposure Analysis for Weekdays and Weekends

class of those at risk from flooding in England and Wales. Sociological Research Online, 12 (4), 4. 6. Fukushima, T. and Nakano, A. (1999) Report on the Hanshin-Awaji Earthquake Disaster, Editorial Committee for the Report on the Hanshin-Awaji Earthquake Disaster, 26-41. 7. Freire, S. (2010) Modeling of spatiotemporal distribution of urban population at high resolution—Value for risk assessment and emergency management. In Geographic Information and Cartography for Risk and Crisis Management; Springer: Berlin, Heidelberg, Germany, 53–67. 8. Freire, S. and Aubrecht, C. (2010) Towards improved risk assessment: Mapping the spatio-temporal distribution of human exposure to earthquake hazard in the Lis-bon metropolitan area, Gi4DM 2010, Turin Italy. 9. Freire, S., Aubrecht, C. and Wegscheider, S. (2011) Spatio-temporal population distribution and evacuation modeling for improving tsunami risk assessment in the Lisbon Metropolitan Area, In Proceedings of 2011 International Symposium on Geoinformation for Disaster Management, Antalya, Turkey, 3–8 May 2011. 10. Freire, S., Florczyk, A. and Ferri, S. (2015) Modeling Day-and Nighttime Population Exposure at High Resolution Application to Volcanic Risk Assessment in Campi Flegrei. – Geospatial Data and Geographical Information Science Proceedings of the ISCRAM 2015 Conference - Kristiansand, May 24-27. 11. Itoigawa, E. et al. (1999) Report on the Hanshin-Awaji Earthquake Disaster, Editorial Committee for the Report on the Hanshin-Awaji Earthquake Disaster, 134-151. 12. Martin, D., Cockings, S. and Leung, S. (2015) Developing a flexible framework for spatiotemporal population modeling, Annals of the Association of American Geographers, 105 (4), 754 – 772. 13. Murao, O. and Yamazaki, F. (2001) Development of fragility curves for building based on damage survey data of a local government after the 1995 Hyogoken-Nanbu Earthquake, Journal of Architecture, Planning and Environmental Engineering, 527, 189-196. 14. NHK Broadcasting Culture Research Institute (2000) NHK Data book 2000 National Time Use Survey. 15. Osaragi, T. and Otani, I. (2009) Estimating spatio-temporal distribution of railroad users and its application to disaster prevention planning, Lecture Notes in Geoinformation and Cartography, Advances in GIScience, Springer, 233-250. 16. Osaragi, T. and Hoshino, T. (2012) Predicting spatiotemporal distribution of transient occupants in urban areas, Lecture Notes in Geoinformation and Cartography, Bridging the Geographic Information Sciences, Eds. J. Gensel et al., Springer, 307-325. 17. Osaragi, T. (2013) Modeling a spatiotemporal distribution of stranded people returning home on foot in the aftermath of a large-scale earthquake, Natural Hazards, Springer, 68(3), 1385-1398. 18. Osaragi, T. (2015) Spatiotemporal distribution of automobile users: Estimation method and applications to disaster mitigation planning, Proceedings of the ISCRAM 2015 Conference, Kristiansand, Eds. L. Palen, M. Buscher, T. Comes, and A. Hughes (http://iscram2015.uia.no/?p=1964). 19. Setagaya Ward (2007) Report on Setagaya land use status quo investigation, City Planning Division, Urban Development Section, Setagaya Ward. 20. Smith, A., Martin, D. and Cockings, S. (2014) Spatio-temporal population modelling for enhanced assessment of urban exposure to flood risk, Applied Spatial Analysis and Policy, 1-19. 21. Smith, A., Newing, A., Quinn, N., Martin, D., Cockings, S. and Neal, J. (2015) Assessing the Impact of Seasonal Population Fluctuation on Regional Flood Risk Management, ISPRS International Journal of Geo- Information, 4, 1118-1141. 22. Tsuji, M. (1982) Estimation of de facto population by indoor population aggregating method: Study on methods to estimate de facto population in small districts Part 2, Journal of Architecture, Planning and Environmental Engineering, 315, 133-142.