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A new perspective on the temporal pattern of human activities in cities: The case of Shanghai
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A new perspective on the temporal pattern of human activities in cities: The case of Shanghai ⁎
Yongping Zhanga,b,c, Lun Liud, , Hui Wange a
Zhou Enlai School of Government, Nankai University, China The Bartlett Centre for Advanced Spatial Analysis, University College London, UK c Experimental Teaching Centre of Applied Social Science, Nankai University, China d Department of Land Economy, University of Cambridge, UK e School of Architecture, Tsinghua University, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Temporal pattern analysis Human activities Kernel density estimation Smart card data Shanghai
Time is a fundamental characteristic for understanding human activities. When analysing temporal pattern of a group of activities, most researchers tend to utilise one temporal attribute when representing time use of activities. Thus, temporal pattern of activities is usually visualised and understood as a profile of various observations listed sequentially over time. This paper aims to investigate the temporal pattern of activities in urban areas from a new perspective. Temporal pattern is visualised and analysed as the distribution of activity points in a two-dimensional temporal plane defined by the start and end time of activities as x and y axes. Kernel density estimation is used as a typical method to observe the temporal pattern of activities in Shanghai based on a oneweek smart card dataset generated in the Shanghai's metro system. The results show that the proposed perspective can reveal considerably more information regarding the temporal pattern than a conventional one can.
1. Introduction Time is a fundamental aspect of human activities. To enrich our understanding of human activities, it is important to analyse their temporal pattern, which involves the examination of various aspects of time use, such as duration, daily rhythm, and so on. Most of existing temporal pattern analysis of activities can be regarded as a time series analysis: an activity is treated as existing at a time point, and thus a temporal pattern of a set of activities is usually understood as a profile of various observations made sequentially through time (e.g. the number of activities across a month). However, as we all know, any activity lasts for a time period rather than just exists at a time point in real situation. Given a set of activities, when the temporal scale of observation is fine enough, treating each activity exist at a time point may impede the appropriate understanding of the set's temporal pattern. To fill this gap in the understanding of temporal patterns of activities, we try to analyse the temporal pattern from a new perspective, i.e., by simultaneously considering the start and end times of activities. From this perspective, a two-dimensional temporal plane is created using the start and end time of activities as x and y axes and each activity is represented as a point on the plane. Correspondingly, the
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temporal pattern of a set of activities can be visualised and analysed as the distribution of activity points in the start time/end time plane, which is, to a certain extent, similar to the analysis of spatial patterns. In spatial analysis, we usually use the coordinates (e.g., in latitude and longitude) to define a spatial location and visualise spatial patterns as the distribution of points in a two-dimensional spatial plane, in which x and y axes represent the coordinates, respectively. We expect that taking into full account the temporal attributes of human activities can enhance the completeness and fineness of analysis and reveal patterns that cannot be observed from a conventional perspective. The paper is organised as follows. Following this introduction, we present a brief review of relevant works (Section 2). We then provide our explanations on methods in Section 3. Section 4 presents study area, data, and results. We end with a brief conclusion and discussion in Section 5. 2. Literature review 2.1. Temporal pattern analysis of human activities Most of existing temporal pattern analysis in geography can be regarded as a time series analysis, which is defined as the body of
Corresponding author at: 19 Silver Street, Cambridge CB3 9EP, UK. E-mail address: [email protected] (L. Liu).
https://doi.org/10.1016/j.cities.2018.10.002 Received 20 March 2018; Received in revised form 29 September 2018; Accepted 4 October 2018 0264-2751/ © 2018 Published by Elsevier Ltd.
Please cite this article as: Zhang, Y., Cities, https://doi.org/10.1016/j.cities.2018.10.002
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collection of chronologically ordered records, each of which comprised of the triple < ID, S, T > , where ID is an object identifier, S are spatial coordinates, and T are sequential time points (Siła-Nowicka et al., 2016). When the detailed trajectory information is ignored or unavailable, an activity can be regarded as a collection of two temporally ordered records each comprised of spatiotemporal information at its start and end time. The most frequently used data are GPS-tracked activities, and activities retrieved from household travel survey. Time Geography, first proposed by Hägerstraand (1970), might be one of most prominent achievements in space-time pattern analysis. It is a powerful framework for observing human activities under various space-time constraints (Chen et al., 2016). Several concepts have been developed to understand an individual's activities in space and time, such as space-time prism, and space-time path (Miller, 2017; Miller, Raubal, & Jaegal, 2016; Yuan, Chen, Li, Shaw, & Lam, 2018). Besides the framework of Time Geography, many other methods and concepts have been proposed to analyse the space-time pattern. Path similarity indexes have been applied to compare individuals' paths (Kwan, Xiao, & Ding, 2014). Demšar and Virrantaus (2010) introduced the concept of 3D space-time density of trajectories to solve the problem of cluttering in the space-time cube. Bach, Shi, Heulot, et al. (2015) introduced time curves as a general approach for visualising the patterns of evolution in temporal data. Ferrante, De Cantis, and Shoval (2018) proposed a general framework for collecting and analysing the tracking data of cruise passengers. For those interested in more information on spacetime pattern analysis, we refer readers to the work conducted by Bach, Dragicevic, Archambault, Hurter, and Carpendale (2014) and An et al. (2015). All these studies distinguish the start and end time of activities, however, there are several differences between the existing studies and our study. First, these studies are more interested in the activities, the spatial positions of which are dynamic changes over time. The methods they used may not be suitable to understand temporal patterns of a large number of activities, which have fixed spatial positions, such as many workers working at a factory. Second, they focus on space-time pattern analysis, rather than the fine-scale temporal pattern analysis – the latter is our focus in this paper and has been ignored to some extent in the existing studies. Third, the start point of these studies is based on spatial dimension, followed by temporal dimension. For example, investigating the dynamic changes of spatial patterns of crime activities over multi-years. However, the start point of our paper is based on temporal dimension, followed by spatial dimension. For example, comparing temporal patterns of working activities across different places. And finally, from a perspective of visualisation, existing studies visualise space-time pattern with two spatial axes representing geographic space, and time added as the third orthogonal axis (Lee, Lee, & Kwan, 2017), while this paper provides a new perspective to visualise temporal pattern mainly by using the start and end time as two axes. Noted that, although there exist several differences about the research focus, pattern delineation, analytical methods, and visualisation between the existing space-time pattern analysis and the temporal pattern analysis discussed in this paper, there is no obvious advantages/disadvantages between them. Generally, the method discussed in this paper will be more suitable if the study has the following features: 1) It focuses on the temporal dimension of activities; 2) Space can be simplified. In other words, each place can be regarded as a point of interest in space, such as a shopping mall or a metro station; and 3) each activity can be defined using its start and end time, and the trajectory analysis of an activity is not the research interest.
statistical methods for analysing time series (Little, 2013). In these studies, the difference between the start and end time of an activity is usually ignored, and an activity is regarded as existing at a time point. As a result, a temporal pattern of a set of activities is usually understood as a profile of observations made sequentially through time (e.g. the number of activities across a month). There are many relevant studies about this kind of temporal pattern analysis. Nelson, Bromley, and Thomas (2001) analysed the temporal pattern of the incidence of violent crime in the city centres of Cardiff and Worcester, the UK, in terms of the day and hour of occurrence. This analysis reveals the specific days and the time of day when levels of violent crimes are at their greatest. Girardin, Calabrese, and Fiore (2008) uncovered the digital footprints of tourists with user-generated content. They found the patterns of below-average activity on weekdays and a rise of presence over the weekend at the Coliseum in Rome, Italy. Luo, Cao, Mulligan, and Li (2016) investigated the temporal characteristics of human mobility with a particular focus on the impact of demographic features using geo-tagged data. Tan, Kwan, and Chai (2017) examined the effects of ethnicity on people's behaviour in the Chinese context. They analysed the temporal rhythms of Hui people's religious activities and the scheduled time for Salah (i.e. religious services) in the mosques of Xining, China. Wu, Ye, Ren, and Du (2018) took advantage of emerging crowdsourcing data and adopted social media check-ins over a 24-h period as a proxy for urban vibrancy. In their case study of Shenzhen, China, they found the evolution of vibrancy is influenced by various factors that are heterogeneous over space and time. Zhang and Mi (2018) delineated the temporal pattern of cycling behaviour using a big dataset of bike trips, with y axis indicating the number of trips per hours and x axis as a timeline. However, as we all know, all activities exist over a period rather than at a time point. The start time and end time are equally important for understanding the time use of an activity and the temporal pattern of a set of activities (Zhang & Liu, 2018; Zhou, Deng, Kwan, & Yan, 2015). When the unit of time measurement is fine enough, the differences between the start and the end time of activities may be large and are not proper to be ignored. In this situation, the ignorance of the difference between activities' start and end time and the adoption of the time series analysis methods may impede the proper understanding of their temporal pattern as a whole. More illustration is given by the following example. We assume that a group of students visit the university library on a given day, each of them has a different entrance and exit time, and the average time staying in the library is 2 h. When a student is in the library, we say he or she is conducting a visiting-library activity. In this example, if the temporal pattern of activities is analysed using only one temporal attribute, such as the start time, the temporal pattern can be treated as the distribution of the number of activities on the time axis. When the unit of time measurement is day, all these activities can be treated as happening at a same time point, since they are all conducted on the same day. However, when the unit of time measurement is hour, it will be difficult to delineate the temporal pattern, for many activities last over several hours, and the pattern will be different based on how we count these activities into different hours. When we analyse the temporal pattern at a finer unit (e.g. minute or second), such bias would be larger. Therefore, treating activities as existing at a time point or adopting the methods belong to time series analysis is not always a good way to understand the temporal pattern of a set of activities. 2.2. Space-time pattern analysis of human activities
2.3. Smart card data analysis of human activities
There are also studies treating the activity as existing during a time period. Generally, most of these studies fall in the domain of space-time pattern analysis, which in this paper is understood as the use of various concepts and methods to describe, visualise, explain, and predict the representation of objects and phenomena with tight space-time integration. From a data perspective, an activity can be regarded as a
Smart card data, generated by automatic fare collection systems, can provide detailed onboard/outboard transactions of a massive population. Compared with traditional travel surveys, smart card data contain a large sample size and are collected automatically, resulting in 2
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being economically saving and efficient (Pelletier et al., 2011). The availability of smart card data provides enormous opportunities for understanding human behaviour. Ma, Wu, Wang, Chen, and Liu (2013) proposed an efficient and effective data-mining procedure that models the travel patterns of transit riders using the transit smart card data. Long, Han, Tu, and Shu (2015) evaluated the effectiveness of the urban growth boundaries in Beijing using smart card data, combined with other big data sources like location check-ins and taxi trajectories. Gao et al. (2018) explored changes in the spatial distribution of the low-tomoderate income group using the transit smart card data. Manley, Zhong, and Batty (2018) adopted the Density Based Scanning Algorithm with Noise (DBSCAN) algorithm to identify clusters of travel events associated with particular individuals whose behaviour over space and time are captured. Most of studies focus on a single city, such as London (the UK) (Manley et al., 2018), Shanghai and Beijing (China) (Long et al., 2015; Zhang, Martens, & Long, 2018), Singapore (Tu et al., 2018), and Seoul (South Korea) (Jung & Sohn, 2017), and Brisbane (Australia) (Zhou, Sipe, Ma, Mateo-Babiano, & Darchen, 2017), but some also give a comparative study about different cities, such as the work conducted by Zhong et al. (2016). There are mainly two differences identified between the research presented in this paper and the existing studies using smart card data. First, most of the existing studies focus on exploring the characteristics of travel behaviour and transport systems in a wider scope. In this paper, we focus on analysing the temporal pattern of activities revealed from smart card data. Second, from a methodological perspective, there are many methods have applied to understand human behaviour using smart card data, from the simple rule-based methods (e.g., Bagchi & White, 2005; Barry, Newhouser, & Rahbee, 2002) to more advanced methods (e.g., Jung & Sohn, 2017; Zhang et al., 2018). Besides, the methods in time series analysis are the main ones to analyse the temporal pattern of mobility (e.g., Ghaemi, Agard, Trépanier, & Partovi Nia, 2017). However, the existing temporal pattern studies fail to simultaneously consider the start and end time, and to adopt the corresponding methods like kernel density estimation into the examination of the temporal pattern.
Fig. 1. Activity points visualised in the start time/end time plane. (For interpretation of the references to colour in this figure, the reader is referred to the web version of this article.)
continuous metro trips. However, according to the empirical data from other Chinese cities, the percentage of sub-tours is < 5% (The 4th Beijing Comprehensive Transport Survey, 2010), therefore, generally speaking, our assumption is reasonable. Based on this definition, we derive human activities from their trip records. 3.3. Kernel density estimation The kernel density estimation function allows one to estimate the intensity of a point pattern and to represent it by means of a smoothed continuous surface (Borruso, 2008). The general form of a kernel estimator is given as
3. Methods 3.1. Basic explanation
λ(s ) =
Fig. 1 illustrates a temporal point pattern in the start time/end time plane. This pattern contains two activities. xa, xb, and ya, yb denote the start and end times of activity A and B, respectively. l is the line y = x, which indicates the unrealistic situation that the start time of an activity is equal to its end time. In reality, all activities should be located in the area between the line l and y axis. The vertical distance from an activity point to line l indicates the duration of an activity, e.g., A's time duration (the green line) is longer than B's (the red line). In addition, the distance between two activities (the blue line) can be understood as a combined measure of the gap between two activities' start times and end times, which demonstrates the temporal simultaneity of two activities.
n
∑i =1
1 ⎛d⎞ k πr 2 ⎝ r ⎠
where λ(s) is the density at temporal location s, r is the search radius, k is the weight of a point i at a time distance d to temporal location s. k is usually modeled as a function (called kernel function) of the ratio between d and r. 4. Study area, data, and results 4.1. Study area and data With an area of 6341 km2 and a population of 24.26 million, Shanghai is one of largest cities in the world (shown in Fig. 2). Shanghai consists of sixteen districts and one county. In 2014, there were fifteen metro lines (including Maglev line) and 313 metro stations. The total length of the metro lines was 577.6 km (Shanghai Institute of Transportation Research, 2015). The metro smart card data used in this paper contain 29,696,363 trips covering a one-week period in 2015 (April 24–30). Each trip record includes the following attributes: card ID, start time, end time, start station, end station, and trip cost. After data pre-processing, we obtained 9,849,904 activities in total. After empirical experiment, we set the cell size for the output raster dataset as 300 ∗ 300 s (i.e. 5 ∗ 5 min), and the search radius as 1800 s (i.e. 30 min), when estimating kernel density in our case study. The setting is able to ensure a good visualisation of temporal pattern, well
3.2. Identify metro-based activities In this paper, we assume a cardholder conducts an activity when he or she exits a metro station from a trip and then enters the same station again for next trip. This activity is assumed to be conducted in the catchment area of the station. Taking a cardholder who has two continuous trips as an example. Sei, Teiare the exit station and time, respectively, for trip i, while Ssi+1, Tsi+1 are the exit station and time, respectively, for trip i + 1. If Sei = Ssi+1, then he or she performs an activity in the catchment area of the station Sei during (Tsi, Tsi+1). Noted that it is also possible that the cardholder conducts a sub-tour by other modes (walk, cycle, car, etc.) to another destination between two 3
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Fig. 2. Map of Shanghai and sample stations.
start time (from 6:30 to 10:00), while late-ending activities (after 21:00) vary less in terms of start time (from 8:00 to 9:00). It is worth noting that a large number of activities in these clusters do not end at 17:00, as expected from a typical eight-hour work programme in China (the so-called zhaojiuwanwu, meaning starting from nine in the morning and ending at five in the evening). Actually, activities in this hotspot end at 18:00 averagely, and they can also end as late as 22:00, which is the closing time of several metro lines. Surprisingly, on weekends, a hotspot of activities is observed at the similar locations as working/schooling activities on weekdays. It is not quite possible that so many people would conduct leisure or shopping activities for such a long time. Therefore, it is reasonable to speculate that many of the activities are overtime working on weekends, which are even not late-starting or early-ending than working/schooling activities on weekdays. These results indicate the existence of a large amount of overtime working in Shanghai, which could be a serious social issue but has not been studied in such a scale before. The hotspots in the morning on weekdays could be shopping, escorting other people, doing personal business, etc. and are likely to be conducted by retirees, housewives/husbands, etc. The start time of the activities in these hotspots ranges from around 8:30 to 11:00, and the end time ranges from around 9:00 to 13:30. The durations of the activities vary between < 10 min and > 4 h. The hotspots in the afternoon extend across a larger area, which is related to a larger variance in the start and end times. The former ranges from around 12:00 to 16:30 and the latter ranges from around 13:00 to 17:00. The durations are generally similar to the morning hotspots. On weekends, the activities in the morning and afternoon are merged into a single cluster, indicating that there is less routine in the life on weekends. The hotspots of nightlife change in shape and intensity across the week. It is the weakest on Monday, and becomes stronger every day till Friday, then decreases again on Sunday.
reveals the characteristics of temporal pattern, and also keeps the efficiency of calculation at a reasonable level. Noted that other combinations of parameters may also be suitable. For the brevity of writing, we won't show the outcomes of other combinations here. Two analyses are conducted here: the temporal patterns of activities at the whole city level through the seven days of a week, and the temporal patterns at different stations across the city. In what follows, we present the results from these two analyses.
4.2. General temporal patterns of activities in Shanghai Fig. 3 shows the conventional measurement of the temporal pattern of activities identified from the metro card data, using the start or end time of activities. In contrast, Fig. 4 shows the visualisation of the temporal pattern using the start and end times simultaneously. It is evident that the latter is considerably more informative than the former. Generally, Fig. 4 shows the activity points are far from evenly distributed on the temporal plane. Four major hotspots can be identified on weekdays, and four different hotspots can be identified on weekends. The most prominent hotspot on weekdays is located in the upper left corner of the plane. The x and y coordinates of these hotspots suggest that the activities last the whole day (from 7–9:00 to 17–19:00) and therefore are very likely to be working or schooling activities. The other three hotspots on weekdays are composed of shorter-duration activities and correspond to the time periods of morning, afternoon and evening, respectively. The most prominent hotspot on weekends is located along the y = x axis, representing a large group of short-duration activities in the morning and afternoon. The other hotspots on weekends include one in the left, one at the upper right corner and one at the top left. The hotspots of working/schooling activities on weekdays are generally wider at the bottom and narrower at the top. This is because early-ending activities (before 18:00) in this group vary more in their 4
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Fig. 3. Temporal pattern of activities based on a representation of a time series.
Xujiahui, indicating a larger proportion of, and temporally more uniform working/schooling activities, while the others show a weaker core. Within the former group, the shapes of the cores also vary considerably. Some cores do not show an obvious direction, such as the red core at East Xujin, meaning that there is no obvious relationship between the end time and the start time of work in these areas. Some cores are partly right-leaning, such as the one at Xujiahui, which are composed of a short horizontal line at approximately 18:00 and a rightleaning cluster above. Such a pattern indicates that people who come earlier to work tend to take off earlier after 18:00 in these areas. The red core at Wujiaochang takes a zigzag shape with three short horizontal lines at approximately 17:50, 18:20 and 19:00 and right-leaning lines in between, which can be interpreted as a mixture of uniform off time and ‘early come early go’. Fig. 5 also demonstrates varying start and end times of working/ schooling activities at different locations. The start time in the red core ranges from 7:00 to 9:00 in certain areas (e.g. Fujin Road and Xincun Road), but only 8:00 to 9:00 in others (e.g. East Xujing, Wujiaochang and Xujiahui). The reason for this finding could be that activities starting between 7:00 and 8:00 are mostly schooling activities and that areas with many schools (compared to job positions) may induce a large number of activities starting in this period. The end times of activities in the hotspots also vary a lot. The end time of the red core ranges from 17:00 to 18:00 at Fujin Road, 17:00 to 19:00 at Wujiaochang and Xincun Road, 17:30 to 19:00 at Xujiahui, and 18:00 to 19:30 at East Xujin. However, certain areas still show a relatively high density of activities starting in the morning even after 20:00, such as Xujiahui and Wujiaochang. It is possible that these activities are combinations of whole-day's working and shopping or leisure activities after work in the same area. However, the travel diary data from Beijing suggest that only 1% workers conduct after-work activities (e.g. grocery shopping or dining) in the adjacent areas (< 1000 m) of the working place (The 4th Beijing Comprehensive Transport Survey, 2010). Therefore, these patterns are likely to indicate a substantial amount of over-time working activities in these areas. In particular, Xujiahui also shows a small hotspot at the top left of the plane, representing a group of activities that start between 9:00 and 9:30 and end at approximately 22:00 when the metro closes. Referring also to the evidence from Beijing, such a cluster can be interpreted as extreme over-time working activities until the workers have to leave to catch the metro. Furthermore, the comparison between the point density in the
4.3. Joint analysis of temporal patterns and spatial locations To illustrate the ability of the proposed perspective in revealing the difference between the activity temporal patterns at different locations, we picked six metro stations in Shanghai, which display quite diverse activity temporal patterns. The stations are Fujin Road, Wujiaochang, East Xujin, Xincun Road, Xujiahui, and Pudong International Airport. Among them, Fujin Road and East Xujin are the north end of Shanghai's Metro Line One and the West end of Metro Line Two, both located outside the outer ring road of the city. Particularly, East Xujin is featured by the newly-built National Expo Centre, where held the Shanghai Auto Expo in our study period. We will be able to see how the activity temporal pattern would look like during a big event by studying East Xujin. In contrast with these outer locations, Wujiaochang and Xujiahui both locate within the mid-ring road and are two of the several commercial and business centres of the city. Besides, Wujiaochang is also featured as a knowledge cluster with renowned universities and institutes such as Tongji and Fudan. Xincun Road also locates within the mid-ring road but is more like an ordinary area of residences with small businesses and public facilities. The last, Pudong International Airport is the major airport of the city and is included as an example to study the activity temporal pattern at a transport hub (Fig. 2). The land uses in 2011 within the 1 km radius of these stations are shown in Table 1. Noted that the land use data are obtained from Shanghai Tongji Institute of Urban Planning and Design, a leading planning institute in China. Newer land uses data are not available for our research purpose. But generally, the urban functions in the catchment areas of the selected stations change slowly. Therefore, the data in 2011 can still give us some background information about the land uses surrounding sample stations. It can be observed that the temporal patterns of activities at different locations vary considerably when plotted out on the start time/ end time plane (Fig. 5). Briefly, the patterns at most places contain a hotspot at the upper left corner of the plane. The x and y coordinates (the start and end times) of these hotspots suggest that the activities last the whole day (from 7–9:00 to 17–19:00) and therefore are highly likely to be working/schooling activities. It is not surprising that working/schooling make up a major part of activities on a weekday; however, it is interesting that the shapes and density distributions vary from place to place. The hotspots at some of these locations contain a relatively big, dense core, including East Xujin, Wujiaochang, and 5
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Fig. 4. Kernel density estimation of activities on each day of a week.
weights of non-work/school activities, such as Xincun Road. The distributions of short-duration activity points at different time periods also vary. Xincun Road shows similar amounts of short-duration activities throughout the day, while Fujin Road have more activities in the morning and early afternoon and Wujiaochang have more activities in the evening. Activity patterns related to special urban functional areas or special events are also evident in the visualisation results. The Pudong International Airport contains a high density of short activities throughout the day, most of which could be picking up and dropping off. East Xujing is the location of the venue for the Shanghai International Auto Expo held from April 20th to 29th in 2015 (the data used in this research were on April 24, 2015). As a result, the temporal pattern of activities at East Xujing takes a triangular shape defined by the opening and closing time of the Expo.
Table 1 Land uses at sample stations. Stations
Fujin Road Wujiaochang East Xujin Xincun Road Xujiahui Pudong International Airport
Land use types Commercial
Residential
Green & water
Others
0.0% 34.6% 3.9% 9.7% 25.3% 0.0%
7.5% 26.2% 9.2% 48.2% 38.7% 0.0%
7.9% 1.1% 4.7% 3.0% 4.5% 0.0%
84.6% 38.1% 82.2% 39.1% 31.5% 100.0%
hotspot and other parts of the plane reveals the ratio between working/ schooling and other short-duration activities in an urban area. For instance, Xujiahui is highly work-dominated with very light-yellow colours outside the hotspots. Xincun Road and Fujin Road show more balanced colours inside and outside the hotspots, indicating larger
5. Conclusion and discussion In this paper, we analyse the temporal pattern of human activities 6
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Fig. 5. Activity temporal patterns at sample stations on 24 April 2015. Note: each sub-figure is coloured on its own scale, to avoid too light colours at places with relatively small numbers of total activities. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Monitoring human movements and activities is one of the basics of smart city management. In current practice, this function is usually implemented using real time density heat maps, from which areas with high and low activity intensity can be identified. However, such crosssectional pattern does not provide information regarding whether the presence of people in an area is short or long stays and the weights, which could further imply the purposes of activities and the functions of urban areas. Here we include another station, the Lujiazui CBD, which is a major business area in Shanghai, and use it and Xujiahui as examples. The red area in Fig. 6 denotes all the on-going activities (population density) identified from the metro card data at 19:00 in these two areas, which were 13,965 and 10,649 activities in total, respectively. In the start time/end time plane, the weights between activities of different durations can be inferred by dividing the red areas with the lines y = x + a (a can be any value between 0 and 24, denoting the
from a new perspective. From this perspective, an activity can be regarded as a point in a two-dimensional temporal plane using the start and end times as x and y axes. Temporal pattern of activities can be visualised and analysed as the distribution of activity points in this plane. Kernel density estimation is applied as a typical method to observe the temporal pattern of human activities in Shanghai using a oneweek metro smart card data. Results demonstrate that the proposed perspective can reveal considerably more information regarding the temporal pattern of activities than conventional perspective, which usually regards the temporal pattern as a profile of observations made sequentially through time. As the proliferation of trace data, the proposed perspective can be developed into an enhanced tool for monitoring and understanding the use of time and space in any given areas (e.g., a city district, a park, or a building) of any cities in the world and contribute to the smart-city agenda. 7
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Fig. 6. Temporal patterns of activities at Lujiazui and Xujiahui.
exists in the world, especially in Eastern Asian countries, like South Korea, and Japan (Kim & Chung, 2016; Park, Kim, & Han, 2017; Yamauchi et al., 2017). This article serves as an initial attempt to understand the temporal pattern of human activities using a two-dimensional temporal pattern and can be extended in many ways. First, the metro card data used in this research covers only a proportion of urban activities and tends to be biased towards activities associated with a relatively long travel distance. Actually, any type of human trace data with temporal information can be applied to conduct this analysis. Given the increasing availability of various urban data, the data source can be largely extended, preferably to those with wider coverage, or geographically/ temporally more fine-grained, such as cell phone data. Furthermore, besides the method of kernel density estimation, more analysis tools can be developed to analyse the temporal pattern. A probable routine is to ‘borrow’ the tools in spatial point pattern analysis, e.g., using the method of nearest neighbour distance to analyse the randomness of distribution (Jun & Kim, 2017), or using the method of spatial autocorrelation coefficient to evaluate the similarity between temporally close activities (Zhou et al., 2015).
duration of activities). In the case of Lujiazui, 1% of the activities ongoing at 19:00 last shorter than 1 h, 12% were between 1and 3 h, 15% were between 3 and 5 h, 5% were between 5 and 7 h, 5% were between 7 and 9 h and 63% last longer than 9 h. In the case of Xujiahui, the corresponding proportions were 2% (last < 1 h), 21% (last 1–3 h), 18% (last 3–5 h), 5% (last 5–7 h), 6% (last 7–9 h) and 47% (last > 9 h). The latter two groups are highly likely to be work or related activities, considering the long duration, while the former ones are more likely to be non-work activities. The results indicate that the Xujiahui CBD attracts a higher proportion of non-work activities in after-work hours, but despite of the many malls and shops, workers are still the major contributors to the population density in these two areas even on a Friday evening. Such information is important for both the purposes of alleviating density in a crowded area and attracting more activities for inactive areas. City managers and business leaders can also use this information to provide tailored services in response to the shifting profile of people in an area. As mentioned in the results, functions of urban areas can also, to an extent, be inferred from the proposed method about activity temporal patterns. Since such inference is based on the actual human behaviour, it should be a better indicator than current, commonly used methods, such as the land uses and the distribution of points of interest, which may not necessarily induce the corresponding amount of actual activities. For instance, we identified different ranges of activity start times in the hotspots of working/schooling activities, which reflects the functional mix between office and education. In the case of Lujiazui and Xujiahui, we are able to tell that Xujiahui attracts a higher proportion of non-work activities during after-work hours and infer that it possesses a stronger commercial and leisure function than Lujiazui. The inference of activity purposes and urban functions based on common sense could contain a substantial level of error at the current stage but can be improved after training with extra data labelled with both the start and end times and the purposes of activities. Other applications of the proposed perspective extend to many aspects of urban management. For example, the number of working activities is informative regarding the actual job positions created in each area; the amount and spatial distribution of overtime working activities can be used to evaluate workers' welfare; and the number of visitors and the types of activities can be used to rate the success of urban sub-districts. Of course, the methodology and all the extension usages are not limited to the case we use. Any city or district with proper data (smart card, cell phone trace, etc.) can produce its own analysis of activity temporal pattern. Furthermore, it would also be interesting to compare the results across cities or countries, e.g. to examine how severe overtime working is in different places of the world, since this issue widely
Acknowledgement This research is funded by the China Scholarship Council (No. 201508060122 & NO. 201306210039), UK ESRC (No. ES/N010981/1), National Natural Science Foundation of China (No. 51478232 & No. 71561137003), and China’s National Key R&D Program (NO. 2017YFB0503500). The authors would like to thank the anonymous referees and editor for their valuable comments that greatly improved this article. References An, L., Tsou, M.-H., Crook, S. E., Chun, Y., Spitzberg, B., Gawron, J. M., & Gupta, D. K. (2015). Space–time analysis: Concepts, quantitative methods, and future directions. Annals of the Association of American Geographers, 105, 891–914. Bach, B., Dragicevic, P., Archambault, D., Hurter, C., & Carpendale, S. (2014). A review of temporal data visualizations based on space-time cube operations. Eurographics Conference on Visualization, 00, 23–41. https://doi.org/10.2312/eurovisstar. 20141171. Bach, B., Shi, C., Heulot, N., ... M, T. (2015). Time curves: Folding time to visualize patterns of temporal evolution in data. ieeexplore.ieee.org. 22, 559–568. Bagchi, M., & White, P. R. (2005). The potential of public transport smart card data. Transport Policy, 12, 464–474. https://doi.org/10.1016/j.tranpol.2005.06.008. Barry, J., Newhouser, R., & Rahbee, A. (2002). Origin and destination estimation in New York City with automated fare system data. (Rec. J. …). Borruso, G. (2008). Network density estimation: A GIS approach for analysing point patterns in a network space. Transactions in GIS, 12, 377–402.
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Cities journal homepage: www.elsevier.com/locate/cities
A new perspective on the temporal pattern of human activities in cities: The case of Shanghai ⁎
Yongping Zhanga,b,c, Lun Liud, , Hui Wange a
Zhou Enlai School of Government, Nankai University, China The Bartlett Centre for Advanced Spatial Analysis, University College London, UK c Experimental Teaching Centre of Applied Social Science, Nankai University, China d Department of Land Economy, University of Cambridge, UK e School of Architecture, Tsinghua University, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Temporal pattern analysis Human activities Kernel density estimation Smart card data Shanghai
Time is a fundamental characteristic for understanding human activities. When analysing temporal pattern of a group of activities, most researchers tend to utilise one temporal attribute when representing time use of activities. Thus, temporal pattern of activities is usually visualised and understood as a profile of various observations listed sequentially over time. This paper aims to investigate the temporal pattern of activities in urban areas from a new perspective. Temporal pattern is visualised and analysed as the distribution of activity points in a two-dimensional temporal plane defined by the start and end time of activities as x and y axes. Kernel density estimation is used as a typical method to observe the temporal pattern of activities in Shanghai based on a oneweek smart card dataset generated in the Shanghai's metro system. The results show that the proposed perspective can reveal considerably more information regarding the temporal pattern than a conventional one can.
1. Introduction Time is a fundamental aspect of human activities. To enrich our understanding of human activities, it is important to analyse their temporal pattern, which involves the examination of various aspects of time use, such as duration, daily rhythm, and so on. Most of existing temporal pattern analysis of activities can be regarded as a time series analysis: an activity is treated as existing at a time point, and thus a temporal pattern of a set of activities is usually understood as a profile of various observations made sequentially through time (e.g. the number of activities across a month). However, as we all know, any activity lasts for a time period rather than just exists at a time point in real situation. Given a set of activities, when the temporal scale of observation is fine enough, treating each activity exist at a time point may impede the appropriate understanding of the set's temporal pattern. To fill this gap in the understanding of temporal patterns of activities, we try to analyse the temporal pattern from a new perspective, i.e., by simultaneously considering the start and end times of activities. From this perspective, a two-dimensional temporal plane is created using the start and end time of activities as x and y axes and each activity is represented as a point on the plane. Correspondingly, the
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temporal pattern of a set of activities can be visualised and analysed as the distribution of activity points in the start time/end time plane, which is, to a certain extent, similar to the analysis of spatial patterns. In spatial analysis, we usually use the coordinates (e.g., in latitude and longitude) to define a spatial location and visualise spatial patterns as the distribution of points in a two-dimensional spatial plane, in which x and y axes represent the coordinates, respectively. We expect that taking into full account the temporal attributes of human activities can enhance the completeness and fineness of analysis and reveal patterns that cannot be observed from a conventional perspective. The paper is organised as follows. Following this introduction, we present a brief review of relevant works (Section 2). We then provide our explanations on methods in Section 3. Section 4 presents study area, data, and results. We end with a brief conclusion and discussion in Section 5. 2. Literature review 2.1. Temporal pattern analysis of human activities Most of existing temporal pattern analysis in geography can be regarded as a time series analysis, which is defined as the body of
Corresponding author at: 19 Silver Street, Cambridge CB3 9EP, UK. E-mail address: [email protected] (L. Liu).
https://doi.org/10.1016/j.cities.2018.10.002 Received 20 March 2018; Received in revised form 29 September 2018; Accepted 4 October 2018 0264-2751/ © 2018 Published by Elsevier Ltd.
Please cite this article as: Zhang, Y., Cities, https://doi.org/10.1016/j.cities.2018.10.002
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collection of chronologically ordered records, each of which comprised of the triple < ID, S, T > , where ID is an object identifier, S are spatial coordinates, and T are sequential time points (Siła-Nowicka et al., 2016). When the detailed trajectory information is ignored or unavailable, an activity can be regarded as a collection of two temporally ordered records each comprised of spatiotemporal information at its start and end time. The most frequently used data are GPS-tracked activities, and activities retrieved from household travel survey. Time Geography, first proposed by Hägerstraand (1970), might be one of most prominent achievements in space-time pattern analysis. It is a powerful framework for observing human activities under various space-time constraints (Chen et al., 2016). Several concepts have been developed to understand an individual's activities in space and time, such as space-time prism, and space-time path (Miller, 2017; Miller, Raubal, & Jaegal, 2016; Yuan, Chen, Li, Shaw, & Lam, 2018). Besides the framework of Time Geography, many other methods and concepts have been proposed to analyse the space-time pattern. Path similarity indexes have been applied to compare individuals' paths (Kwan, Xiao, & Ding, 2014). Demšar and Virrantaus (2010) introduced the concept of 3D space-time density of trajectories to solve the problem of cluttering in the space-time cube. Bach, Shi, Heulot, et al. (2015) introduced time curves as a general approach for visualising the patterns of evolution in temporal data. Ferrante, De Cantis, and Shoval (2018) proposed a general framework for collecting and analysing the tracking data of cruise passengers. For those interested in more information on spacetime pattern analysis, we refer readers to the work conducted by Bach, Dragicevic, Archambault, Hurter, and Carpendale (2014) and An et al. (2015). All these studies distinguish the start and end time of activities, however, there are several differences between the existing studies and our study. First, these studies are more interested in the activities, the spatial positions of which are dynamic changes over time. The methods they used may not be suitable to understand temporal patterns of a large number of activities, which have fixed spatial positions, such as many workers working at a factory. Second, they focus on space-time pattern analysis, rather than the fine-scale temporal pattern analysis – the latter is our focus in this paper and has been ignored to some extent in the existing studies. Third, the start point of these studies is based on spatial dimension, followed by temporal dimension. For example, investigating the dynamic changes of spatial patterns of crime activities over multi-years. However, the start point of our paper is based on temporal dimension, followed by spatial dimension. For example, comparing temporal patterns of working activities across different places. And finally, from a perspective of visualisation, existing studies visualise space-time pattern with two spatial axes representing geographic space, and time added as the third orthogonal axis (Lee, Lee, & Kwan, 2017), while this paper provides a new perspective to visualise temporal pattern mainly by using the start and end time as two axes. Noted that, although there exist several differences about the research focus, pattern delineation, analytical methods, and visualisation between the existing space-time pattern analysis and the temporal pattern analysis discussed in this paper, there is no obvious advantages/disadvantages between them. Generally, the method discussed in this paper will be more suitable if the study has the following features: 1) It focuses on the temporal dimension of activities; 2) Space can be simplified. In other words, each place can be regarded as a point of interest in space, such as a shopping mall or a metro station; and 3) each activity can be defined using its start and end time, and the trajectory analysis of an activity is not the research interest.
statistical methods for analysing time series (Little, 2013). In these studies, the difference between the start and end time of an activity is usually ignored, and an activity is regarded as existing at a time point. As a result, a temporal pattern of a set of activities is usually understood as a profile of observations made sequentially through time (e.g. the number of activities across a month). There are many relevant studies about this kind of temporal pattern analysis. Nelson, Bromley, and Thomas (2001) analysed the temporal pattern of the incidence of violent crime in the city centres of Cardiff and Worcester, the UK, in terms of the day and hour of occurrence. This analysis reveals the specific days and the time of day when levels of violent crimes are at their greatest. Girardin, Calabrese, and Fiore (2008) uncovered the digital footprints of tourists with user-generated content. They found the patterns of below-average activity on weekdays and a rise of presence over the weekend at the Coliseum in Rome, Italy. Luo, Cao, Mulligan, and Li (2016) investigated the temporal characteristics of human mobility with a particular focus on the impact of demographic features using geo-tagged data. Tan, Kwan, and Chai (2017) examined the effects of ethnicity on people's behaviour in the Chinese context. They analysed the temporal rhythms of Hui people's religious activities and the scheduled time for Salah (i.e. religious services) in the mosques of Xining, China. Wu, Ye, Ren, and Du (2018) took advantage of emerging crowdsourcing data and adopted social media check-ins over a 24-h period as a proxy for urban vibrancy. In their case study of Shenzhen, China, they found the evolution of vibrancy is influenced by various factors that are heterogeneous over space and time. Zhang and Mi (2018) delineated the temporal pattern of cycling behaviour using a big dataset of bike trips, with y axis indicating the number of trips per hours and x axis as a timeline. However, as we all know, all activities exist over a period rather than at a time point. The start time and end time are equally important for understanding the time use of an activity and the temporal pattern of a set of activities (Zhang & Liu, 2018; Zhou, Deng, Kwan, & Yan, 2015). When the unit of time measurement is fine enough, the differences between the start and the end time of activities may be large and are not proper to be ignored. In this situation, the ignorance of the difference between activities' start and end time and the adoption of the time series analysis methods may impede the proper understanding of their temporal pattern as a whole. More illustration is given by the following example. We assume that a group of students visit the university library on a given day, each of them has a different entrance and exit time, and the average time staying in the library is 2 h. When a student is in the library, we say he or she is conducting a visiting-library activity. In this example, if the temporal pattern of activities is analysed using only one temporal attribute, such as the start time, the temporal pattern can be treated as the distribution of the number of activities on the time axis. When the unit of time measurement is day, all these activities can be treated as happening at a same time point, since they are all conducted on the same day. However, when the unit of time measurement is hour, it will be difficult to delineate the temporal pattern, for many activities last over several hours, and the pattern will be different based on how we count these activities into different hours. When we analyse the temporal pattern at a finer unit (e.g. minute or second), such bias would be larger. Therefore, treating activities as existing at a time point or adopting the methods belong to time series analysis is not always a good way to understand the temporal pattern of a set of activities. 2.2. Space-time pattern analysis of human activities
2.3. Smart card data analysis of human activities
There are also studies treating the activity as existing during a time period. Generally, most of these studies fall in the domain of space-time pattern analysis, which in this paper is understood as the use of various concepts and methods to describe, visualise, explain, and predict the representation of objects and phenomena with tight space-time integration. From a data perspective, an activity can be regarded as a
Smart card data, generated by automatic fare collection systems, can provide detailed onboard/outboard transactions of a massive population. Compared with traditional travel surveys, smart card data contain a large sample size and are collected automatically, resulting in 2
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being economically saving and efficient (Pelletier et al., 2011). The availability of smart card data provides enormous opportunities for understanding human behaviour. Ma, Wu, Wang, Chen, and Liu (2013) proposed an efficient and effective data-mining procedure that models the travel patterns of transit riders using the transit smart card data. Long, Han, Tu, and Shu (2015) evaluated the effectiveness of the urban growth boundaries in Beijing using smart card data, combined with other big data sources like location check-ins and taxi trajectories. Gao et al. (2018) explored changes in the spatial distribution of the low-tomoderate income group using the transit smart card data. Manley, Zhong, and Batty (2018) adopted the Density Based Scanning Algorithm with Noise (DBSCAN) algorithm to identify clusters of travel events associated with particular individuals whose behaviour over space and time are captured. Most of studies focus on a single city, such as London (the UK) (Manley et al., 2018), Shanghai and Beijing (China) (Long et al., 2015; Zhang, Martens, & Long, 2018), Singapore (Tu et al., 2018), and Seoul (South Korea) (Jung & Sohn, 2017), and Brisbane (Australia) (Zhou, Sipe, Ma, Mateo-Babiano, & Darchen, 2017), but some also give a comparative study about different cities, such as the work conducted by Zhong et al. (2016). There are mainly two differences identified between the research presented in this paper and the existing studies using smart card data. First, most of the existing studies focus on exploring the characteristics of travel behaviour and transport systems in a wider scope. In this paper, we focus on analysing the temporal pattern of activities revealed from smart card data. Second, from a methodological perspective, there are many methods have applied to understand human behaviour using smart card data, from the simple rule-based methods (e.g., Bagchi & White, 2005; Barry, Newhouser, & Rahbee, 2002) to more advanced methods (e.g., Jung & Sohn, 2017; Zhang et al., 2018). Besides, the methods in time series analysis are the main ones to analyse the temporal pattern of mobility (e.g., Ghaemi, Agard, Trépanier, & Partovi Nia, 2017). However, the existing temporal pattern studies fail to simultaneously consider the start and end time, and to adopt the corresponding methods like kernel density estimation into the examination of the temporal pattern.
Fig. 1. Activity points visualised in the start time/end time plane. (For interpretation of the references to colour in this figure, the reader is referred to the web version of this article.)
continuous metro trips. However, according to the empirical data from other Chinese cities, the percentage of sub-tours is < 5% (The 4th Beijing Comprehensive Transport Survey, 2010), therefore, generally speaking, our assumption is reasonable. Based on this definition, we derive human activities from their trip records. 3.3. Kernel density estimation The kernel density estimation function allows one to estimate the intensity of a point pattern and to represent it by means of a smoothed continuous surface (Borruso, 2008). The general form of a kernel estimator is given as
3. Methods 3.1. Basic explanation
λ(s ) =
Fig. 1 illustrates a temporal point pattern in the start time/end time plane. This pattern contains two activities. xa, xb, and ya, yb denote the start and end times of activity A and B, respectively. l is the line y = x, which indicates the unrealistic situation that the start time of an activity is equal to its end time. In reality, all activities should be located in the area between the line l and y axis. The vertical distance from an activity point to line l indicates the duration of an activity, e.g., A's time duration (the green line) is longer than B's (the red line). In addition, the distance between two activities (the blue line) can be understood as a combined measure of the gap between two activities' start times and end times, which demonstrates the temporal simultaneity of two activities.
n
∑i =1
1 ⎛d⎞ k πr 2 ⎝ r ⎠
where λ(s) is the density at temporal location s, r is the search radius, k is the weight of a point i at a time distance d to temporal location s. k is usually modeled as a function (called kernel function) of the ratio between d and r. 4. Study area, data, and results 4.1. Study area and data With an area of 6341 km2 and a population of 24.26 million, Shanghai is one of largest cities in the world (shown in Fig. 2). Shanghai consists of sixteen districts and one county. In 2014, there were fifteen metro lines (including Maglev line) and 313 metro stations. The total length of the metro lines was 577.6 km (Shanghai Institute of Transportation Research, 2015). The metro smart card data used in this paper contain 29,696,363 trips covering a one-week period in 2015 (April 24–30). Each trip record includes the following attributes: card ID, start time, end time, start station, end station, and trip cost. After data pre-processing, we obtained 9,849,904 activities in total. After empirical experiment, we set the cell size for the output raster dataset as 300 ∗ 300 s (i.e. 5 ∗ 5 min), and the search radius as 1800 s (i.e. 30 min), when estimating kernel density in our case study. The setting is able to ensure a good visualisation of temporal pattern, well
3.2. Identify metro-based activities In this paper, we assume a cardholder conducts an activity when he or she exits a metro station from a trip and then enters the same station again for next trip. This activity is assumed to be conducted in the catchment area of the station. Taking a cardholder who has two continuous trips as an example. Sei, Teiare the exit station and time, respectively, for trip i, while Ssi+1, Tsi+1 are the exit station and time, respectively, for trip i + 1. If Sei = Ssi+1, then he or she performs an activity in the catchment area of the station Sei during (Tsi, Tsi+1). Noted that it is also possible that the cardholder conducts a sub-tour by other modes (walk, cycle, car, etc.) to another destination between two 3
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Fig. 2. Map of Shanghai and sample stations.
start time (from 6:30 to 10:00), while late-ending activities (after 21:00) vary less in terms of start time (from 8:00 to 9:00). It is worth noting that a large number of activities in these clusters do not end at 17:00, as expected from a typical eight-hour work programme in China (the so-called zhaojiuwanwu, meaning starting from nine in the morning and ending at five in the evening). Actually, activities in this hotspot end at 18:00 averagely, and they can also end as late as 22:00, which is the closing time of several metro lines. Surprisingly, on weekends, a hotspot of activities is observed at the similar locations as working/schooling activities on weekdays. It is not quite possible that so many people would conduct leisure or shopping activities for such a long time. Therefore, it is reasonable to speculate that many of the activities are overtime working on weekends, which are even not late-starting or early-ending than working/schooling activities on weekdays. These results indicate the existence of a large amount of overtime working in Shanghai, which could be a serious social issue but has not been studied in such a scale before. The hotspots in the morning on weekdays could be shopping, escorting other people, doing personal business, etc. and are likely to be conducted by retirees, housewives/husbands, etc. The start time of the activities in these hotspots ranges from around 8:30 to 11:00, and the end time ranges from around 9:00 to 13:30. The durations of the activities vary between < 10 min and > 4 h. The hotspots in the afternoon extend across a larger area, which is related to a larger variance in the start and end times. The former ranges from around 12:00 to 16:30 and the latter ranges from around 13:00 to 17:00. The durations are generally similar to the morning hotspots. On weekends, the activities in the morning and afternoon are merged into a single cluster, indicating that there is less routine in the life on weekends. The hotspots of nightlife change in shape and intensity across the week. It is the weakest on Monday, and becomes stronger every day till Friday, then decreases again on Sunday.
reveals the characteristics of temporal pattern, and also keeps the efficiency of calculation at a reasonable level. Noted that other combinations of parameters may also be suitable. For the brevity of writing, we won't show the outcomes of other combinations here. Two analyses are conducted here: the temporal patterns of activities at the whole city level through the seven days of a week, and the temporal patterns at different stations across the city. In what follows, we present the results from these two analyses.
4.2. General temporal patterns of activities in Shanghai Fig. 3 shows the conventional measurement of the temporal pattern of activities identified from the metro card data, using the start or end time of activities. In contrast, Fig. 4 shows the visualisation of the temporal pattern using the start and end times simultaneously. It is evident that the latter is considerably more informative than the former. Generally, Fig. 4 shows the activity points are far from evenly distributed on the temporal plane. Four major hotspots can be identified on weekdays, and four different hotspots can be identified on weekends. The most prominent hotspot on weekdays is located in the upper left corner of the plane. The x and y coordinates of these hotspots suggest that the activities last the whole day (from 7–9:00 to 17–19:00) and therefore are very likely to be working or schooling activities. The other three hotspots on weekdays are composed of shorter-duration activities and correspond to the time periods of morning, afternoon and evening, respectively. The most prominent hotspot on weekends is located along the y = x axis, representing a large group of short-duration activities in the morning and afternoon. The other hotspots on weekends include one in the left, one at the upper right corner and one at the top left. The hotspots of working/schooling activities on weekdays are generally wider at the bottom and narrower at the top. This is because early-ending activities (before 18:00) in this group vary more in their 4
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Fig. 3. Temporal pattern of activities based on a representation of a time series.
Xujiahui, indicating a larger proportion of, and temporally more uniform working/schooling activities, while the others show a weaker core. Within the former group, the shapes of the cores also vary considerably. Some cores do not show an obvious direction, such as the red core at East Xujin, meaning that there is no obvious relationship between the end time and the start time of work in these areas. Some cores are partly right-leaning, such as the one at Xujiahui, which are composed of a short horizontal line at approximately 18:00 and a rightleaning cluster above. Such a pattern indicates that people who come earlier to work tend to take off earlier after 18:00 in these areas. The red core at Wujiaochang takes a zigzag shape with three short horizontal lines at approximately 17:50, 18:20 and 19:00 and right-leaning lines in between, which can be interpreted as a mixture of uniform off time and ‘early come early go’. Fig. 5 also demonstrates varying start and end times of working/ schooling activities at different locations. The start time in the red core ranges from 7:00 to 9:00 in certain areas (e.g. Fujin Road and Xincun Road), but only 8:00 to 9:00 in others (e.g. East Xujing, Wujiaochang and Xujiahui). The reason for this finding could be that activities starting between 7:00 and 8:00 are mostly schooling activities and that areas with many schools (compared to job positions) may induce a large number of activities starting in this period. The end times of activities in the hotspots also vary a lot. The end time of the red core ranges from 17:00 to 18:00 at Fujin Road, 17:00 to 19:00 at Wujiaochang and Xincun Road, 17:30 to 19:00 at Xujiahui, and 18:00 to 19:30 at East Xujin. However, certain areas still show a relatively high density of activities starting in the morning even after 20:00, such as Xujiahui and Wujiaochang. It is possible that these activities are combinations of whole-day's working and shopping or leisure activities after work in the same area. However, the travel diary data from Beijing suggest that only 1% workers conduct after-work activities (e.g. grocery shopping or dining) in the adjacent areas (< 1000 m) of the working place (The 4th Beijing Comprehensive Transport Survey, 2010). Therefore, these patterns are likely to indicate a substantial amount of over-time working activities in these areas. In particular, Xujiahui also shows a small hotspot at the top left of the plane, representing a group of activities that start between 9:00 and 9:30 and end at approximately 22:00 when the metro closes. Referring also to the evidence from Beijing, such a cluster can be interpreted as extreme over-time working activities until the workers have to leave to catch the metro. Furthermore, the comparison between the point density in the
4.3. Joint analysis of temporal patterns and spatial locations To illustrate the ability of the proposed perspective in revealing the difference between the activity temporal patterns at different locations, we picked six metro stations in Shanghai, which display quite diverse activity temporal patterns. The stations are Fujin Road, Wujiaochang, East Xujin, Xincun Road, Xujiahui, and Pudong International Airport. Among them, Fujin Road and East Xujin are the north end of Shanghai's Metro Line One and the West end of Metro Line Two, both located outside the outer ring road of the city. Particularly, East Xujin is featured by the newly-built National Expo Centre, where held the Shanghai Auto Expo in our study period. We will be able to see how the activity temporal pattern would look like during a big event by studying East Xujin. In contrast with these outer locations, Wujiaochang and Xujiahui both locate within the mid-ring road and are two of the several commercial and business centres of the city. Besides, Wujiaochang is also featured as a knowledge cluster with renowned universities and institutes such as Tongji and Fudan. Xincun Road also locates within the mid-ring road but is more like an ordinary area of residences with small businesses and public facilities. The last, Pudong International Airport is the major airport of the city and is included as an example to study the activity temporal pattern at a transport hub (Fig. 2). The land uses in 2011 within the 1 km radius of these stations are shown in Table 1. Noted that the land use data are obtained from Shanghai Tongji Institute of Urban Planning and Design, a leading planning institute in China. Newer land uses data are not available for our research purpose. But generally, the urban functions in the catchment areas of the selected stations change slowly. Therefore, the data in 2011 can still give us some background information about the land uses surrounding sample stations. It can be observed that the temporal patterns of activities at different locations vary considerably when plotted out on the start time/ end time plane (Fig. 5). Briefly, the patterns at most places contain a hotspot at the upper left corner of the plane. The x and y coordinates (the start and end times) of these hotspots suggest that the activities last the whole day (from 7–9:00 to 17–19:00) and therefore are highly likely to be working/schooling activities. It is not surprising that working/schooling make up a major part of activities on a weekday; however, it is interesting that the shapes and density distributions vary from place to place. The hotspots at some of these locations contain a relatively big, dense core, including East Xujin, Wujiaochang, and 5
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Fig. 4. Kernel density estimation of activities on each day of a week.
weights of non-work/school activities, such as Xincun Road. The distributions of short-duration activity points at different time periods also vary. Xincun Road shows similar amounts of short-duration activities throughout the day, while Fujin Road have more activities in the morning and early afternoon and Wujiaochang have more activities in the evening. Activity patterns related to special urban functional areas or special events are also evident in the visualisation results. The Pudong International Airport contains a high density of short activities throughout the day, most of which could be picking up and dropping off. East Xujing is the location of the venue for the Shanghai International Auto Expo held from April 20th to 29th in 2015 (the data used in this research were on April 24, 2015). As a result, the temporal pattern of activities at East Xujing takes a triangular shape defined by the opening and closing time of the Expo.
Table 1 Land uses at sample stations. Stations
Fujin Road Wujiaochang East Xujin Xincun Road Xujiahui Pudong International Airport
Land use types Commercial
Residential
Green & water
Others
0.0% 34.6% 3.9% 9.7% 25.3% 0.0%
7.5% 26.2% 9.2% 48.2% 38.7% 0.0%
7.9% 1.1% 4.7% 3.0% 4.5% 0.0%
84.6% 38.1% 82.2% 39.1% 31.5% 100.0%
hotspot and other parts of the plane reveals the ratio between working/ schooling and other short-duration activities in an urban area. For instance, Xujiahui is highly work-dominated with very light-yellow colours outside the hotspots. Xincun Road and Fujin Road show more balanced colours inside and outside the hotspots, indicating larger
5. Conclusion and discussion In this paper, we analyse the temporal pattern of human activities 6
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Fig. 5. Activity temporal patterns at sample stations on 24 April 2015. Note: each sub-figure is coloured on its own scale, to avoid too light colours at places with relatively small numbers of total activities. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Monitoring human movements and activities is one of the basics of smart city management. In current practice, this function is usually implemented using real time density heat maps, from which areas with high and low activity intensity can be identified. However, such crosssectional pattern does not provide information regarding whether the presence of people in an area is short or long stays and the weights, which could further imply the purposes of activities and the functions of urban areas. Here we include another station, the Lujiazui CBD, which is a major business area in Shanghai, and use it and Xujiahui as examples. The red area in Fig. 6 denotes all the on-going activities (population density) identified from the metro card data at 19:00 in these two areas, which were 13,965 and 10,649 activities in total, respectively. In the start time/end time plane, the weights between activities of different durations can be inferred by dividing the red areas with the lines y = x + a (a can be any value between 0 and 24, denoting the
from a new perspective. From this perspective, an activity can be regarded as a point in a two-dimensional temporal plane using the start and end times as x and y axes. Temporal pattern of activities can be visualised and analysed as the distribution of activity points in this plane. Kernel density estimation is applied as a typical method to observe the temporal pattern of human activities in Shanghai using a oneweek metro smart card data. Results demonstrate that the proposed perspective can reveal considerably more information regarding the temporal pattern of activities than conventional perspective, which usually regards the temporal pattern as a profile of observations made sequentially through time. As the proliferation of trace data, the proposed perspective can be developed into an enhanced tool for monitoring and understanding the use of time and space in any given areas (e.g., a city district, a park, or a building) of any cities in the world and contribute to the smart-city agenda. 7
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Fig. 6. Temporal patterns of activities at Lujiazui and Xujiahui.
exists in the world, especially in Eastern Asian countries, like South Korea, and Japan (Kim & Chung, 2016; Park, Kim, & Han, 2017; Yamauchi et al., 2017). This article serves as an initial attempt to understand the temporal pattern of human activities using a two-dimensional temporal pattern and can be extended in many ways. First, the metro card data used in this research covers only a proportion of urban activities and tends to be biased towards activities associated with a relatively long travel distance. Actually, any type of human trace data with temporal information can be applied to conduct this analysis. Given the increasing availability of various urban data, the data source can be largely extended, preferably to those with wider coverage, or geographically/ temporally more fine-grained, such as cell phone data. Furthermore, besides the method of kernel density estimation, more analysis tools can be developed to analyse the temporal pattern. A probable routine is to ‘borrow’ the tools in spatial point pattern analysis, e.g., using the method of nearest neighbour distance to analyse the randomness of distribution (Jun & Kim, 2017), or using the method of spatial autocorrelation coefficient to evaluate the similarity between temporally close activities (Zhou et al., 2015).
duration of activities). In the case of Lujiazui, 1% of the activities ongoing at 19:00 last shorter than 1 h, 12% were between 1and 3 h, 15% were between 3 and 5 h, 5% were between 5 and 7 h, 5% were between 7 and 9 h and 63% last longer than 9 h. In the case of Xujiahui, the corresponding proportions were 2% (last < 1 h), 21% (last 1–3 h), 18% (last 3–5 h), 5% (last 5–7 h), 6% (last 7–9 h) and 47% (last > 9 h). The latter two groups are highly likely to be work or related activities, considering the long duration, while the former ones are more likely to be non-work activities. The results indicate that the Xujiahui CBD attracts a higher proportion of non-work activities in after-work hours, but despite of the many malls and shops, workers are still the major contributors to the population density in these two areas even on a Friday evening. Such information is important for both the purposes of alleviating density in a crowded area and attracting more activities for inactive areas. City managers and business leaders can also use this information to provide tailored services in response to the shifting profile of people in an area. As mentioned in the results, functions of urban areas can also, to an extent, be inferred from the proposed method about activity temporal patterns. Since such inference is based on the actual human behaviour, it should be a better indicator than current, commonly used methods, such as the land uses and the distribution of points of interest, which may not necessarily induce the corresponding amount of actual activities. For instance, we identified different ranges of activity start times in the hotspots of working/schooling activities, which reflects the functional mix between office and education. In the case of Lujiazui and Xujiahui, we are able to tell that Xujiahui attracts a higher proportion of non-work activities during after-work hours and infer that it possesses a stronger commercial and leisure function than Lujiazui. The inference of activity purposes and urban functions based on common sense could contain a substantial level of error at the current stage but can be improved after training with extra data labelled with both the start and end times and the purposes of activities. Other applications of the proposed perspective extend to many aspects of urban management. For example, the number of working activities is informative regarding the actual job positions created in each area; the amount and spatial distribution of overtime working activities can be used to evaluate workers' welfare; and the number of visitors and the types of activities can be used to rate the success of urban sub-districts. Of course, the methodology and all the extension usages are not limited to the case we use. Any city or district with proper data (smart card, cell phone trace, etc.) can produce its own analysis of activity temporal pattern. Furthermore, it would also be interesting to compare the results across cities or countries, e.g. to examine how severe overtime working is in different places of the world, since this issue widely
Acknowledgement This research is funded by the China Scholarship Council (No. 201508060122 & NO. 201306210039), UK ESRC (No. ES/N010981/1), National Natural Science Foundation of China (No. 51478232 & No. 71561137003), and China’s National Key R&D Program (NO. 2017YFB0503500). The authors would like to thank the anonymous referees and editor for their valuable comments that greatly improved this article. References An, L., Tsou, M.-H., Crook, S. E., Chun, Y., Spitzberg, B., Gawron, J. M., & Gupta, D. K. (2015). Space–time analysis: Concepts, quantitative methods, and future directions. Annals of the Association of American Geographers, 105, 891–914. Bach, B., Dragicevic, P., Archambault, D., Hurter, C., & Carpendale, S. (2014). A review of temporal data visualizations based on space-time cube operations. Eurographics Conference on Visualization, 00, 23–41. https://doi.org/10.2312/eurovisstar. 20141171. Bach, B., Shi, C., Heulot, N., ... M, T. (2015). Time curves: Folding time to visualize patterns of temporal evolution in data. ieeexplore.ieee.org. 22, 559–568. Bagchi, M., & White, P. R. (2005). The potential of public transport smart card data. Transport Policy, 12, 464–474. https://doi.org/10.1016/j.tranpol.2005.06.008. Barry, J., Newhouser, R., & Rahbee, A. (2002). Origin and destination estimation in New York City with automated fare system data. (Rec. J. …). Borruso, G. (2008). Network density estimation: A GIS approach for analysing point patterns in a network space. Transactions in GIS, 12, 377–402.
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