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Research and application of space-time behavior maps: a review Research and application of space-time behavior maps: a review
Zhang, Xia; Cheng, Zhi; Tang, Luliang; Xi, Jinglong
2021-09-03 00:00:00
JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 2021, VOL. 20, NO. 5, 581–595 https://doi.org/10.1080/13467581.2020.1800473 URBAN PLANNING AND DESIGN a a b a Xia Zhang , Zhi Cheng , Luliang Tang and Jinglong Xi a b School of Urban Design, Wuhan University, Wuhan, China; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China ABSTRACT ARTICLE HISTORY Received 23 October 2019 Today, in the space-time big data environment, the research into behavior maps, as an Accepted 16 July 2020 important spatial analysis and behavior visualization method, has gradually increased in dimension, depth and breadth. In this study, we used the China National Knowledge KEYWORDS Infrastructure (CNKI) database and the Web of Science (WOS) Core Collection database as the Behavior visualization; main literature search engines, through the literature review, the behavior maps analysis type, behavior maps; space-time visual expression, and its method evolution are summarized. And we used CiteSpace and behavior VOSviewer scientific knowledge mapping software to reveal the hotspots, development trends, and field dynamics of space-time behavior maps research in the collected documents. We find that the current research literature on space-time behavior maps shows three areas of cluster- ing: post-occupancy evaluation, public open space, and time geography. By establishing the typical application cases in the three fields, the applications and development frontiers are summarized and considered. 1. Introduction data. The behavior visualization methods have had considerable development in both the space-time The behavior map is a direct behavior visualization dimension and the application extension depth. In method that observes or records an individual’s beha- this study, we used CiteSpace and VOSviewer knowl- vior (location, activity characteristics, and other infor- edge mapping analysis software to comprehensively mation) and links it to various parts of the surrounding sort the research into space-time behavior maps environment (Jialin, Yajing, and Dongmei 2018). through massive document data mining and analysis, A behavior map is the product of observations, and systematically analyzing the hotspots, development also a tool for spatial analysis and design. The behavior trends, and dynamic fields over the past 40 years, and map was first proposed by Ittelson et al. (Ittelson 1976) combining and summarizing the application cases of in the late 1960s, and was originally applied to record behavior maps, in order to lay a foundation for the various behavioral characteristics of people in build- research into and application of space-time behavior ings to help designers connect design features with maps. human behavior in time and space. Subsequently, classic applications such as Jan Gehl’s Public Space and Public Life (PSPL) survey (Jan et al. 2003) and 2. Overview of behavior maps research Kevin Lynch’s work in the field of urban imagery 2.1. The two types of behavior maps (Kevin, Yiping, and Xiaojun 2001) have been widely applied in the field of architecture. The behavior map As the earliest used behavior visualization research has five characteristics (Ittelson 1996): ① the plan is method, behavior maps can be divided into two cate- clear. ② the behavior of the target individual has the gories according to the research objects and applica- data, description and clear mark in the position. ③ It tion purpose, namely, “person-centered” and “specific uses the daily table to record the continuous time. ④ It environment-centered” (Klein et al. 2018). In this paper, has scientific program guidance for observation and we consider two types of behavior visualization analy- record. ⑤ It has a system for encoding, marking and sis: “person-centered” and “space-centered”. counting. The rise of computers has brought about technolo- 2.1.1. Person-centered gical improvements in behavior maps research. The “Person-centered” behavior visualization analysis development of big data, cloud computing and geo- mainly focuses on or tracks the behavioral activities graphic information science in the information and of specific individuals. Researchers generally need to communication era has further strengthened the abil- obtain the written consent of the research subjects to ity to acquire, analyze and apply space-time behavior conduct long-term observational studies, so this CONTACT Zhi Cheng chengzhi93@yeah.net School of Urban Design, Wuhan University, Wuhan 430072, China © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the Architectural Institute of Japan, Architectural Institute of Korea and Architectural Society of China. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 582 X. ZHANG ET AL. Table 1. Examples of visual representation of a behavior map. Symbol code + Three-dimensional Graphical floor plan spatial structure slice sample graph method is time-consuming and labor-intensive. By Ittelson et al.) (Ittelson 1970), to express the behavioral tracking individual behavioral paths, we can obtain characteristics and laws of the subjects studied, and information about their habits, personality, and beha- has been subsequently applied to study the relation- vioral characteristics, usually in the form of a chart. For ship between student activities and open spaces example, Blennerhassett et al. (Blennerhassett et al. (Beeken and Janzen 1978), the evaluation of the use 2018) observed the behavior of the elderly occupants of special care spaces (Bell and Smith 1997), the impact of a nursing home throughout the day, and evaluated of open spaces on children’s health (Nilda, Robin, and the most suitable space for the rehabilitation of the Mohammed 2010), the exploration of the activities of elderly in nursing homes. When studying different patients during rehabilitation in the built environment scenarios, the complexity of the behavior can vary (Blennerhassett et al. 2018), the impact of the operat- according to the research object. It is also possible to ing room layout on care workers (Bayramzadeh et al. integrate multiple analysis methods, such as question- 2018), and the study of the characteristics of the life naires, interviews, etc. habits of Japanese individuals and families over the course 1 day, 1 month, and 1 year (Ellegård et al. 2019). 2.1.2. Space-centered 2.2.2. Symbol code + floor plan This type of behavior visualization analysis is designed to (Barker, 1980) proposed that some basic conditions observe or document the behavioral patterns of people in must be fulfilled before any behavioral activity can be a particular environment or space and to analyze the types recorded. For example, an accurate scale map of the of activities that occur during a certain time period. This observation area, the type of observation activity defini - method is often used by landscape architects and urban tion, a coding system rule, and the repeated observa- designers, and is primarily used to study public open tions planned for a specific time. This was the earliest space. When Jan Gehl (1965) (Jan and Renke 2002) studied behavior map representation using symbolic coding the behavioral characteristics of people on the Piazza del plus floor planning (Table 1). This approach has subse- Popolo in Italy, he used space-centered behavior visualiza- quently been applied in the study of urban plaza spaces tion analysis. Through the space-centered behavior map, (Golicnik 2005; Gharib and Salama 2014; Ngesan and he found that people tended to stand on the edge of the Zubir 2015), kindergarten outdoor spaces (Fernandes square space, such as the pillars of the arch, or under the and Elali 2008; Raymundo, Kuhnen, and Soares 2010; arch or along the facade of the building. This behavior was Smith et al. 2014), and urban street spaces (Elsheshtawy later described as a “boundary effect”. Similarly, when 2013). This approach focuses more on the human activ- Goličnik (Golicnik 2005) studied Bristo Square in ities taking place in a specific environment. Edinburgh, he took into account the behavioral conflicts between skateboarders and visitors to the square. In order to better serve these two groups of people, the behavior 2.2.3. Three-dimensional spatial structure slice map method was used to record the behavioral character- (Yanwei 2014) used three-dimensional spatial structure istics of the skateboarders and visitors, so that visualization (Table 1) to describe the individual space- a reasonable area of public facilities could be designed. time trajectory. The X- and Y-coordinates of the spatial entity represent the spatial plane, and the third attri- bute of the point data is used to represent the event 2.2. Visual representations of a behavior map information; that is to say, the time is represented by 2.2.1. Graphical the Z-coordinate. The activities of the same individual The graphical chart (Table 1) was first introduced in the occurring at different times are connected in series to design of the Psychiatric Rehabilitation Institute (by form a continuous overall individual behavior path JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 583 characteristic. Three-dimensional graphs can more (Petrenko et al. 2013). In 2016, Huang Weixin at the intuitively express the trinity of people, time and Department of Building Technology Science of space. In describing the events occurring in a scene, Tsinghua University, China, also realized dynamic real- it is necessary to consider the change of the flow time data visualization based on a WiFi positioning activity before, during and after the event (Luliang system (Weixin 2016). et al. 2019). The three-dimensional expression of the Over times, behavioral information categories and spatial structure slice can display the relationship data collection methods have undergone a series of between before and after the event in a graph. It can changes, and the corresponding behavioral visualization accurately express the changes in people’s time and data and tools are constantly changing and progressing. space. In chronological order (Figure 1), ① Environment-based: the area is swept quickly, all actions are marked on the map and recorded once every time. This can be used to 2.3. Behavior maps method evolution determine the use of each area in the space. ② Chart- based: When the environmental characteristics are not Behavior maps are visual representations of behavioral the main focus, the time data are mainly used to calculate data and the related information of graphical repre- the behavior on the chart. ③ Traces-based: observing the sentations (Zube and Moore 2013). In 1948, the traces after the activity to record the behavior occurring American psychologist E.C. Tolman first proposed in the area. ④ CARS, SOPLAY, SOPARC, and OSRAC-P: a “cognitive map” in his “Cognitive maps in rats and Four visualization models in behavioral research, which men” paper, which explained how both animals and are mainly used to study children’s activities. ⑤ GPS/GIS/ humans form a model similar to a live map in their RFID/WLAN: four modern digital information technolo- mind, and obtain a comprehensive representation of gies that can track pedestrian walking and cycling beha- the environment through their perceptual experience. vior track. ⑥ Time-lapse video technology: used to record In 1960, the famous American urban planner Kevin the behavior data of vehicles or pedestrians using a space Lynch proposed the five elements of marker, node, for a long period of time. region, border, and road that constitute cognitive maps in his book “The Image of the City” (Kevin, Yiping, and Xiaojun 2001). In 1970, Bill Hillier, 3. Research focus and frontier trend analysis Professor of the Bartlett School of Architecture at the of space-time behavior maps University College of London, proposed space syntax theory, which studies the behavioral factors in the non- In our study, the China National Knowledge spontaneously formed space and indirectly represents Infrastructure (CNKI) database and the Web of the spatial information such as visibility and accessi- Science (WOS) Core Collection database were used as bility (Bill, Chris, and Fang 2005). At the same time, the main data sources, and keywords related to “beha- William H. Ittelson of the University of Arizona pre- vior map” were selected (the search time was up to sented the “Behavior Map Method” for the first time 31 August 2019). The journal literature, Ph.D. and in the field of visualization of behavioral features to Master’s paper, and related conference papers about assist in architectural design and space research behavior maps published between 1981 and 2019 (Ittelson 1976). In recent years, with the development were obtained. The results were then identified, of digital information technology and big data applica- screened, and corrected. Finally, a total of 916 records tions, massive data acquisition methods and various were obtained (538 articles from CNKI and 378 from types of visual analysis tools have emerged. Behavior WOS) as the data source of this review. CiteSpace soft- map research has once again reached a climax. In 2013, ware was used to map the scientific knowledge maps Anastasia Petrenko of the Department of Geography of space-time behavior maps from 1981 to 2019. It was and Planning at the University of Saskatchewan, also used to analyze the source countries, keywords, Canada, used sensors and WiFi-based positioning sys- and citations of the relevant research literature, and to tems to track and analyze pedestrian trajectories, summarize the research overview and hotspots of enabling visualization of the behavioral trajectories space-time behavior maps. We used the cluster Figure 1. Development timeline of behavior maps methods. 584 X. ZHANG ET AL. Figure 2. Number of articles in the literature from 1981 to 2019 on space-time behavior maps research. analysis function of VOSviewer to reveal the frontier research increased year by year, and it broke trends and field dynamics of space-time behavior map through 100 articles for the first time in 2017. research. From the perspective of the spatial distribution characteristics, the countries and regions involved in space-time behavior maps research are mainly 3.1. Research focus the United States, Japan, China, and some European countries. Taking 2 years as a time slice By combining all the collected literature data, we and taking the country as a node, the national could explore the temporal and spatial character- time slice map of the space-time behavior map istics of space-time behavior maps research. From research was drawn (see Figure 3). It can be the perspective of the temporal distribution char- found that the United States has the largest num- acteristics, the number of papers published on ber of publications and the highest centrality space-time behavior maps has shown an increasing (0.33), indicating that its research has high aca- trend, year by year (Figure 2), which indicates that demic value. China’s publication volume is second the research on space-time behavior maps has only to the United States, but its centrality is only gradually increased in recent years. Before 2009, 0.08, which shows that China has more room for the number of papers per year remained below research and development in the field of space- 25. After 2010, there was an obvious research time behavior map research. boom. The number of publications related to the Figure 3. Space-time behavior maps study literature source country time-slice map. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 585 Figure 4. Keyword co-occurrence relationship map of the non-Chinese space-time behavior maps research. 3.2. Research hotspots research into space-time behavior maps in non- Chinese countries, when using CiteSpace to conduct The scientific issues explored in a group of papers with the co-occurrence analysis of research topics, the key- relatively large numbers and intrinsic correlations can words in the non-Chinese literature and the noun key- be seen as a hot topic of academic research during this words in the abstracts were selected as network nodes period of time (Chen 2004). Abstracts and keywords are for the visualization, from which we can see that the hot the refined expressions of the subject and content of the topic keywords in the field of non-Chinese space-time article, and their relevance can reflect the research hot- behavior maps research are (Figure 4): “time geogra- spots in this research field, to a certain extent. The phy”, “post-occupancy evaluation”, “public open CiteSpace software was used to produce scientific space”, “GIS”, “physical activity”, “walking”, “big data”, knowledge maps of the collected literature data, which “global positioning system”, and “social network”. At the can basically show the research hotspots in this field. same time, using the timeline view analysis function in CiteSpace, the co-occurrence timeline evolution map of 3.2.1. Non-chinese research hotspots the topics studied with regard to non-Chinese space- Only 378 non-Chinese documents were retrieved time behavior maps was drawn (see Figure 5), from through the WOS Core Collection database. In order to which it can be found that research on “time geogra- obtain more comprehensive information on the phy” was a hot topic after 1994. For example, the Figure 5. Keyword co-occurrence timeline evolution map of the non-Chinese space-time behavior maps research. 586 X. ZHANG ET AL. application of the time geography time-space prism 3.2.2. Chinese research hotspots concept to geographic accessibility (Miller 1991), the The number of related documents collected through measurement of quantitative accessibility (Handy and the CNKI database was 538. However, the database Niemeier 1997), improved traditional accessibility based export literature lacks cited information, and only the on point data and space-time path analysis research keywords of the existing 538 documents could be used (Kwan 1998), a more detailed study of temporal and as a network node for the co-citation analysis. Since spatial behavior and accessibility from a female perspec- keywords or topics such as “behavior map”, “behavior tive (Kwan 1999b, 1999a), and a conceptual view of time data visualization”, “space-time behavior map” and and space from the perspective of physical geography “space-time behavior visualization” were searched for, and human geography (Massey 1999). Research on after eliminating these keywords, hot topical themes in “post-occupancy evaluation” became a hotspot after the field of Chinese behavior maps research were 2004, such as the use of post-occupancy assessments (Figure 6): “post-occupancy evaluation” “time geogra- to study the quality of public housing (Liu 2003), explor- phy” “public open space” “GIS” “big data” “landscape ing the similarities and differences between post-use architecture” “time and space path” “time and space assessments and on-site research methods for building information”, and “environmental behavior”. At the thermal comfort (Nicol and Roaf 2005), and post- same time, the co-occurrence timeline evolution map occupancy assessment studies of residential spaces of Chinese space-time behavior maps research was also using radio frequency identification (RFID) technology drawn (Figure 7). It was found that research on “time (Gillott et al. 2006). After 2007, “GIS” and “public open geography” was hot after 2000, in studies such as the space” became hot topics, in studies such as exploring introduction of time geography origins, basic concepts, potential human activities and individual activity inter- methods, and applications (Yanwei and Enzhou 1997; actions in physical and virtual spaces through space- Yanwei 1998) and an overview of the application of time time GIS methods (Shaw and Yu 2007, 2008) and explor- geography and possible future applications (Yanwei ing individual large-scale space-time data. Other pub- et al. 2000a; Yanwei and Hua 2000b). After 2004, the lications covered space-time GIS visualization methods research on “post-occupancy evaluation” became (Shaw, Yu, and Bombom 2008), an assessment of the a hotspot, in studies such as the introduction of post- bio-climate comfort of the public open spaces in Lisbon occupancy evaluation research in the development of (Oliveira and Andrade 2007), the relationship between residential theory (Shuoxian and Chaohui 2004), the urban park space design and use (Goličnik and Ward introduction of the post-occupancy evaluation concept, Thompson 2010), the study of racially mixed community history, method, basic operation mode, and future public space leisure activities, the exploration of the application prospects (Jing and Shaoxue 2005), and extent of inter-ethnic interaction (Peters 2010), and the the development characteristics of post-occupancy eva- use of behavior maps and GIS techniques to study pub- luation in foreign countries and its applicability in China lic open space behavioral occupancy patterns (Marušić (Donghan 2006). After 2007, “public open space” and 2011). “GIS” became hotspots. For example, post-occupancy Figure 6. Keyword co-occurrence relationship map of Chinese space-time behavior maps research. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 587 Figure 7. Keyword co-occurrence timeline evolution map of Chinese space-time behavior map research. evaluation research on established environment and of space-time behavior maps research. Among them urban public open space (Jianhua 2007), research on (Figure 9), red represents the post-occupancy evalua- the evaluation index system of urban public open space tion research field, green represents the public open quality and application (Ning et al. 2008), the use of the space research field, and blue represents the time survey data of daily activities of urban residents in geography research field. The following is a brief over- Beijing in 2007 to test data models and methods view of the dynamics of the three major areas, in order through GIS tools (Yanwei et al. 2009), the exploration to introduce and summarize the typical application of the application of GIS-based geographic computing cases in various fields. and 3D geographic visualization methods in time geo- graphy (Kwan et al. 2010), research on the application of 4.1. Post-occupancy evaluation applications GIS-based geographic narrative methods in space-time behavior (Kwan et al. 2013), and research on space-time The high-frequency keywords in this field are “architec- GIS methods and summarizing the quantitative analysis ture”, “Physical Activity”, “User Behavior”, “Indoor of human behavior problems (Shaw and Zhixiang 2014; Positioning System”, “Global Positioning System”, Shaw, Jie, and Feng 2016). “Indicator System”, “Indoor Environment Quality”, and “GPS data” (Figure 9, red area). The dynamics of this field are mainly focused on the use of new post- 3.3. Frontier trends occupancy evaluation new methods based on behavioral By introducing the collected 916 records into the data visualization, in addition to the basic methods of VOSviewer for overlay visualization analysis, the fron- post-occupancy evaluation. The following are three tier theme and trend relationship map of space-time related research applications: behavior map research were obtained (Figure 8), from Case 1: (Gillott et al. 2006) conducted a post- which we can find that “public health”, “daily activ- occupancy evaluation based on the relationship between ities”, “living space”, “elderly”, “big data”, “quantitative the use of new residential space in the UK and the chan- analysis”, “smart city”, “data mining”, and “text mining” ging lifestyle needs of consumers. The RFID tracking are the most cutting-edge topics in the study of space- system was used in the evaluation to collect the occu- time behavior maps since 2016. At the same time, it pancy data of each space in the house by tracking the can be seen that the derivative development trends RFID reader and wristband tags worn by each family surrounding the research hotspot of “time geography” member. Overall, the project was very successful, provid- are “GIS”, “time and space information”, “information ing information for developers on flexible open spaces visualization”, and “time and space trajectory data”. for small homes, as well as the recent market trends. Case 2: (Petrenko et al. 2013). GPS cannot accurately obtain the location of indoor personnel positioning, so 4. Behavior maps research application and this case study used an indoor WiFi positioning system case analysis and multi-sensor smartphone platform, with the assis- Three core clustering views of space-time behavior tance of 37 volunteers. After a one-month experiment, map research were obtained by the cluster analysis of this study successfully tracked and collected more than VOSviewer. This also revealed three dynamic clusters 36 million pedestrian indoor trajectories, and realized 588 X. ZHANG ET AL. Figure 8. Frontier themes and VOSviewer trend relationship map of space-time behavior maps research. the visualization of the behavior trajectories by centrally behavior”, “child”, “elderly”, “spatial behavior pattern”, processing and analyzing the behavioral big data and “time and space trajectory data” (Figure 9, green (Figures 10 and 11). The results not only revealed the area). The research in this field mainly investigates the relationship between individual spatial behavior and relationship between public open spaces and healthy decision-making, but also helped to significantly living, walking, and the use of population characteris- improve building management, emergency operations, tics. The following are three related research and security controls. applications: Case 3: (Weixin and Lijing 2018) took a pair of retired Case 1: (Lestan, Eržen, and Golobič 2014) studied couples as an example, using ultra-wideband (UWB) the relationships between public open space quality technology to conduct a three-week study on the and healthy lifestyles in residential development pro- indoor living behavior of the users, and finally refined jects in Ljubljana, Slovenia, from the perspective of and quantitatively analyzed the residential behavior vulnerable groups such as the elderly and children. data (Figure 12). The daily activities of family members The study, through behavior mapping and resident and the changes in their behavioral timing were opinion surveys, confirmed the differences in open obtained, and the ratios of the time spent by family spaces in selected residential areas and their relation- members in various functional spaces were counted. ship with outdoor activities, and also revealed a range This precise indoor positioning technology not only of socioeconomic variables (such as education and revealed some neglected residential behavior patterns economic conditions) for people’s physical activity and potential residential needs, but also provided an and the strong influence on the perceived health early basis for the design of residential spaces; it also status. provided some quantitative data support for future Case 2: The Environmental Behavior and Space architectural design. Design (EBSD) research group (Xia, Haoran, and Chang 2018) took the open space of the Sakura Mountain Top area of Wuhan University as the research object, and 4.2. Public open space applications analyzed the influence of the behavior pattern of both tourist and students in the space. A smartphone trajec- The high-frequency keywords in this field are “public tory recording app was used to track the tourists and health”, “walking”, “visualization”, “environmental JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 589 Figure 9. Topic keyword VOSviewer clustering analysis map of space-time behavior maps research. Figure 10. Aggregate trajectories drawn by participants in the Petrenko et al. (Petrenko et al. 2013) study; darker areas correspond to indoor campus paths (emphasis added). Figure 11. (a) 2D view of the trajectories recorded in the Petrenko et al. (Petrenko et al. 2013) study; (b) 3D view of the trajectories, with the arrows corresponding to the directions of travel. 590 X. ZHANG ET AL. students in this space, combined with photographs and were collected. The behavior trajectory analysis on-site questionnaires. Finally, based on a GIS platform, and origin-destination (OD) cost matrix analysis the data were analyzed and processed from three method were used to analyze the open status, aspects: route interference, line-of-sight interference, use status, and open demand of campus boundary and domain interference (Figure 13). Conclusions were space. The analysis, supplemented by question- then made based on the optimization of the landscape naires and field research, selected the boundary space for students and tourists, and a recommendation with open conditions to propose a spatial optimi- for the best view point in the space was made zation strategy. (Figure 14). Case 4: (Jing 2018) took the historical protection Case 3: The EBSD research group also con- area of 1.76 km in AnDingmen Street of Beijing as ducted research on the Wuhan University campus the research object, and established the spatial indi- public space security map and the campus bound- cators of the behavior of the elderly, according to ary space open strategy. In the initial study, five the three factors of sunshine, green space, and operability indicators (space visibility, functional humanities, and the established medical, entertain- mixture, space environment, space accessibility, ment, catering, and shopping facilities. A physical and space vitality) were selected from the various space evaluation system was developed at the level influencing factors for public space security. Based of transportation, financial services, and education. on the GIS platform, a public space security map Through the GIS platform used to track the activ- of the campus (day and night) was drawn (Figure ities of the elderly and identify the street view 15), and then the trajectory recording app was images, 741 scenes were finally visualized (Figure used to collect and analyze the behavior trajec- 16), and the physical space attribute information tories of teachers and students on the campus, was obtained. Principal component analysis and supplemented by questionnaire survey to verify regression analysis were then used to obtain the the rationality of the security map. In the later main impact of the physical space of the elderly. research, GPS trajectory data, OpenStreetMap This explained how these factors affect the beha- road data, and point of interest (POI) facility data vioral space mechanism. Figure 12. (a) Schematic diagram of house layout and equipment installation recorded in the Weixin and Lijing (Weixin and Lijing 2018) study; (b) one-day male owner activity space distribution heat map; (c) One-day host activity space distribution heat map. Figure 13. The EBSD study (Xia, Haoran, and Chang 2018) of the Sakura Mountain Top area of Wuhan University: (a) line of sight interference trajectory line distribution; (b) line interference trajectory line distribution; (c) field interference trajectory line distribution. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 591 Figure 14. The EBSD study (Xia, Haoran, and Chang 2018) of the Sakura Top Area of Wuhan University: (a) semantic analysis of visitor stay behavior; (b) best viewpoint recommendation. Figure 15. (a) The campus public space security map for Wuhan University (daytime); (b) The campus public space security map for Wuhan University (nighttime). Figure 16. The match of four main components density of physical space and behavioral Space(Jing 2018). “data mining”, and “smart city” (Figure 9, blue area). 4.3. Time geography applications The field dynamics mainly focus on the application of The high-frequency keywords in this field are “GIS”, 3D information visualization methods in time geogra- “big data”, “space-time prism”, “time and space infor- phy and the related research based on space-time mation”, “time and space path”, “data visualization”, behavior data visualization analysis. 592 X. ZHANG ET AL. Case 1: (Kwan 2005) studied 216 (100 male, 116 data were adopted to realize a full-time modeling female) residents with driver’s license with an average analysis of the urban geospatial space and behavior age of 42.6 from 100 families by a GPS mobile data space in the pre-, and post-stages of the concert collection test in the Lexington area of the United (Figure 18). The results shown that the method is States. The information was collected for each respon- effective and innovative in analyzing the spatial and dent on a Saturday trip, totaling 2,758 travel records temporal evolution process of an urban activity event. and 794,861 data points with latitude and longitude and time information. The GIS-based 3D visualization function then generated a 3D space-time path map of 5. Discussion and conclusion a female sample without children under 16 years old 5.1. Discussion (Figure 17), revealing that the travel was mainly along highways and main roads, showing that the 3D visua- Combining the development of the space-time beha- lization can reveal different space-time behavior vior maps research with the existing research and patterns. application results, it shows the following develop- Case 2: The EBSD research group (Xia et al. 2016) ment trends: used 28 road landscapes in Wuhan as the experimental First, the advent of the era of big data and the object. Through the collection and analysis of network continuous development of information and commu- heat data such as road landscape search volume, sign- nication technology (ICT) have prompted the space- ins and social media likes, a comprehensive road land- time behavior maps research to show the trend of scape evaluation model was established. The landscape multi-source research data, diversification of research view field was also combined to obtain the road “beauty means, individualization of research objects and spe- degree”. In addition, the “most beautiful path optimiza- cific of research topics, which will make the application tion algorithm” was designed and implemented based of behavior maps to unprecedented levels. on the distance and time constraints, and the most Second, behavior maps have broken through the beautiful path algorithm was compared with the tradi- limits of qualitative analysis and the simple application tional shortest path algorithm. The results shown that in of traditional data in the development of multidisci- the same situation of start and end points, the most plinary integration. This will allow us to reveal the beautiful path algorithm could find a more beautiful spatial and temporal relationships between individuals path within the distance (time) threshold constraint, and social life through multidimensional dynamic dis- and could thus improve the pleasure in the travel plays of space-time behavior data, and will allow us to process. explore the temporal and spatial patterns of people’s Case 3: (Luliang et al. 2019), took the 2015 Jay Chou behavior patterns hidden behind the data. World Tour Concert (Wuhan Station) as an example of Finally, in the practical application of behavior a major urban event, and proposed a city event space- maps, “People-oriented” conception will be valued. time model analysis method that combines real-world The space-time behavior maps’ visual expression of traffic data with social media data. The GPS trajectory individual behaviors makes it a medium for people- data of offline taxis in Wuhan and online microblog oriented conception to be more specifically placed in Figure 17. Space-time paths based on GPS data from the Lexington area of the United States (Kwan 2005). JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 593 Figure 18. Space-time expression model of a concert event in Wuhan (Luliang et al. 2019). the practice of urban planning and architectural categories and the advancement of data acquisition design. It also enables space-time behavior research, tools have promoted the evolution of behavior maps space planning and design, built environment with method. From environment-based, chart-based to reconstructed, use and post-occupancy evaluation model-based and video-based technologies, behavior form a benign working cycle together. Based on the visualization methods have had considerable develop- benign working cycle, it will improve the quality of ment in both the space-time dimension and the appli- human settlement environment. cation extension depth. Third, through comparative analysis, on the one hand, there is an upward trend in space-time beha- 5.2. Conclusion vior maps research; on the other hand, compared with the study of non-Chinese behavior maps in the In this paper, the concept and types of behavior maps time dimension around 1994, Chinese research was have been first briefly summarized. The development lagging, but it is currently in a research boom. At and evolution of behavior maps research methods has the same time, the hotspot evolution process of also been described. Based on the CNKI database and space-time behavior maps is basically the same, the WOS Core Collection database, CiteSpace and which is divided into three stages, that is, the VOSviewer software were used to visualize the hot- study stage of space-time behavior from the per- spots and the frontier trends of space-time behavior spective of time geography, POE (post-occupancy maps research. Three dynamic clusters of space-time evaluation) study stage and public open space behavior maps research were revealed by cluster study stage. In the study stage of time geography, analysis. GIS, spatiotemporal trajectory data and spatiotem- The main contributions of this study can be sum- poral information visualization are becoming the marized as follows: latest research hotspots in space-time behavior First, behavior maps were initially used as maps in China. a direct and observational behavior visualization Fourth, the research and application of the existing research method. According to the research objects space-time behavior maps has mainly focused on the and application purposes, behavior maps can be fields of post-occupancy evaluation of buildings, pub- divided into “person-centered“ and “space-centered lic open space, and time geography. In general, beha- “. These two types of behavior visualization analysis vior maps have more research results in geography basically summarize the research and application of and urban scale of time geography, and in architec- behavior maps. tural scale of post-occupancy evaluation; while Second, the rise of computers, the change of tech- research into urban mesoscale of public open space nical means and the loading of information and data has greater potential for application. have made the visual representation of behavior maps from the initial simple two-dimensional graphical chart representation to the use of complex symbol code + Disclosure statement floor plan representation, and finally towards the three-dimensional expression of space-time structure. No potential conflict of interest was reported by the authors. At the same time, the diversity of information data 594 X. ZHANG ET AL. Funding Goličnik, B., and C. 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