Environment and Planning B: Urban Analytics and City Science
Benchmark : Making the Measurement of Public Life Open Source
Journal: Environment and Planning B: Urban Analytics and City Science ManuscriptFor ID EPB-2018-0108 Review Only Manuscript Type: Urban Systems Design: from “Science for Design” to “Design in Science”
Keywords: urban design, smart cities, sensors, big data, DIY
https://mc04.manuscriptcentral.com/epb
Page 1 of 39 Environment and Planning B: Urban Analytics and City Science 1 1 2 3 4 Benchmark 5 6 7 8 9 10 Open Sourcing the Measurement of Public Life 11 12 13 14 Submission 15 16 17 Urban Systems Design: from “Science for Design” To “Design in Science” 18 19 For Review Only 20 Abstract (250 word limit) 21 22 23 Urban Designers have measured the quality of urban spaces since the field began. With the advent of new 24 25 sensors technologies—cell phone traces, Wi Fi, Bluetooth, image analytics—there is a wealth of data 26 27 available to measure the dynamics of daily life. Many experiments in using this data come from private 28 29 and public partnerships that are top down and make data inaccessible to the average citizen. Benchmark 30 31 set out to test whether Do it Yourself (DIY) sensors could be embedded into street furniture to make it 32 33 easy for anyone to collect data on the quality of public space. Using the Gehl method to guide the type of 34 35 36 data collected, the Benchmark project developed sensors embedded street furniture to measure the quality 37 38 of public space. The vision analytics, made available by new machine learning algorithms, worked the 39 40 best and captured multiple types of data defined by the Gehl methods, and through enhancing these 41 42 algorithms, we provided a dataset anyone can use to analyze urban space. Like any prototype, some 43 44 improvements can be made to both types of sensors included in the box as well as to the ease of bench 45 46 assembly. Benchmark shows combining human observations with automated observations can provide a 47 48 new way for urban designers to measure public space. 49 50 51 52 53 54 55 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Environment and Planning B: Urban Analytics and City Science Page 2 of 39 2 1 2 3 I. Introduction 4 5 6 Developing methods for measuring public spaces that result in decisions about how spaces should be used 7 8 9 has been of interest to urban designers since the field was established. Data analysis techniques involve 10 11 counting the number of people who pass through the site, creating maps of environmental features such as 12 13 shade and noise, and interviewing people. These analog technologies combined with video analysis were 14 15 employed by Kevin Lynch (1981) and William Whyte (1980) (1980) in their seminal work, which 16 17 changed how we viewed the value of public space. Sensor networks, such as cell phones and the Internet 18 19 of Things (IoT), have createdFor datasets Reviewthat radically change Onlyour ability to measure the urban environment 20 21 both in time and space. However, this new data can often be hard to obtain, and when it is available, it is 22 23 usually aggregated to a level where it is hard to evaluate smaller public areas such as parks or tactical 24 25 urbanism projects that activate the public realm. 26 27 28 29 Benchmark seeks to address these issues by creating a simple, downloadable toolkit that measures the 30 31 quality of urban space using a set of sensors embedded in a bench. Benchmark’s furniture, sensors, and 32 33 software are all Do It Yourself (DIY), allowing anyone to replicate. The Benchmark toolkit measures 34 35 “Public Life,” a term often referenced in Jan Gehl’s (2013) work to refer to citizens’ daily interactions 36 37 with others within the built environment. The Gehl methodology records the “human scale of people’s 38 39 activities and interactions” (Gehl and Svarre, 2013: 3)using in person observations. Benchmark tested 40 41 whether these measurements could be automated through sensors embedded in street furniture. Software 42 43 developed for Benchmark uses machine learning techniques to analyze the data, which is then 44 45 transformed into visualizations to make the data easier to understand. Results of three field tests showed 46 47 48 applying machine learning techniques to images acquired from Benchmark proved to have the best results 49 50 for measuring public life over other sensors embedded in the bench. Benchmark was simple for others to 51 52 replicate but could use some design refinements to make it easier for users to build themselves as well as 53 54 to process the data. Ultimately the first release of Benchmark showed machine learning has potential for 55 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Page 3 of 39 Environment and Planning B: Urban Analytics and City Science 3 1 2 3 measuring public space and making the data more accessible to the public and the development of this 4 5 DIY tool can help urban designers measure public space. 6 7 8 The Benchmark project is framed within the literature of measuring open space. Therefore, this paper 9 10 begins by introducing those methodologies. The prevalence of sensor networks has provided more data 11 12 13 for measuring interaction in public space, and a review of how these tools have been employed to perform 14 15 that work will be provided. Given the work set out to develop DIY sensors, a background in these 16 17 technologies will be provided along with a review of sensors already embedded in street furniture to 18 19 measure urban spaces. BenchmarkFor was Review both an investigation Only of the design of the furniture itself and test 20 21 of its ability to measure public space. Details on the design logic of the sensors and benches as well as 22 23 implementation of the bench during three field tests will be presented in the paper along with an analysis 24 25 of the results. The Benchmark project used data visualization strategies to communicate the data 26 27 analytics, and these strategies show how urban designers can use the study as evidence to improve the 28 29 quality of public space. The data analytics are followed by a discussion of the impact sensors and machine 30 31 32 learning techniques can have on measuring urban space, improvements that could be made both to the 33 34 bench design and algorithms, and how the data can be used by those who try to impact public life in a 35 36 positive way. 37 38 39 Measuring Public Life 40 41 42 Measuring Public Space 43 44 45 Measuring how well public space performs is perhaps as old as the field of urban design. Modernist urban 46 47 planning, with the tower in the park ideals, sparked a renewed interest in qualities we value in urban space 48 49 50 as many of these projects failed to create vibrant communities. Jane Jacobs (1961) was perhaps the most 51 52 prominent advocate for shifting how urban planners create value in the public realm. She believed the 53 54 physical conditions of the city—mixed use, small blocks, age of buildings, and density—could induce the 55 56 city’s vitality and diversity. She argued there needs to be an “intricate mutual support” between the 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Environment and Planning B: Urban Analytics and City Science Page 4 of 39 4 1 2 3 analysis on the social behavior of the real life in the cities and the economic behavior of the city (Jacobs, 4 5 1961: 14). Her analysis of neighborhood parks and streets was one of the first to link macro analysis of 6 7 social interactions with the physical characteristics of the urban design. Jacobs based her work on 8 9 empirical studies of city neighborhoods, focusing on the way people inhabited space and created vibrant 10 11 12 communities. Her methods were in direct juxtaposition with the modernist planning techniques of the 13 14 time, which often lacked human observation. Ultimately, she changed ways cities are observed and 15 16 understood. 17 18 19 Following Jacob’s lead,For urban Review scholars such as Lynch Only (1960)(1960) introduced more systematic 20 21 frameworks for qualitative and quantitative measures to study the link between spatial elements, the 22 23 meaning it bears in the resident’s perceptions of the city, and the activities in those cities. Lynch was 24 25 interested in studying the mental image citizens have of the city by focusing on the visual quality, namely 26 27 the “legibility” of the physical elements of the cityscape (Lynch, 1960: 2). Lynch, therefore, created a 28 29 methodology in which he asked residents to draw a mental map of what was important to them in the city. 30 31 32 Collectively these mental maps showed what he called paths, edges, districts, nodes, and landmarks, and 33 34 these urban elements help make a city more cohesive. Drawing on these observations, Lynch developed 35 36 urban design plans focused on making these elements more legible. 37 38 39 Jan Gehl’s Life Between Buildings (Gehl, 2011) examined the relationship between activities of 40 41 public life and the physical character of the environment. He classified the activities that appear in public 42 43 space as “necessary activities,” “optional activities,” and “social activities” and argued that, while 44 45 necessary activities occur regardless of the environment, optional activities only occur when the quality of 46 47 the physical environment is optimal (Gehl, 2011: 9). Gehl believed that social activities cannot be studied 48 49 in isolation as they are intertwined with the necessary and optional activities that unfold in public space. 50 51 52 This desire to understand the intricate relationship between the built environment and different kinds of 53 54 activities brought Gehl and Svarre (2013) to introduce an array of tools that can be used to study public 55 56 life. Their method relies primarily on direct manual observations documented through maps, counts, and 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Page 5 of 39 Environment and Planning B: Urban Analytics and City Science 5 1 2 3 photographs, which produces both qualitative and quantitative observations that ultimately result in 4 5 “thick” descriptions of the social activities in public space. Figure 1 is an example of output from a study 6 7 in Melbourne done using the Gehl method. Gehl Architects specializes in working with cities to measure 8 9 10 these social interactions and ultimately help them re design public spaces based on their analysis. 11 12 13 14 15 16 17 18 19 For Review Only 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 Figure 1. Image from Gehl study of public life on the street corner so Melbourne the data was collected through a collaboration with the city 43 44 where 44 sensors were installed across the city. (Visualizing Pedestrian Activity in the City of Melbourne MORPHOCODE, n.d.) 45 46 William Whyte’s The Social Life of Small Urban Places (1980) similarly focused on the interactions 47 48 between people in public space. Whyte conducted a comparative analysis across various public spaces in 49 50 New York City to understand why some places are more attractive for public life over others. Whyte 51 52 53 mixed a variety of methods including firsthand observations, notations, interviews, and secondhand 54 55 observation using still and movie photography. While the time lapse photography allows for the 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Environment and Planning B: Urban Analytics and City Science Page 6 of 39 6 1 2 3 quantification of the spatial and temporal density of the usage in public space, the descriptions of the 4 5 context and the interviews provide qualitative insight of the rationale behind such behavior. (Figure 2) 6 7 One of the findings from Whyte’s study was people were interested in developing their own social spaces 8 9 10 and the ability to move furniture helped to create new ways for groups to socialize and maximize the 11 12 potential use of public space. The success of the plaza study lead to the development of New York City 13 14 Zoning parameters that allowed for greater density if public space was included in the site design. 15 16 17 18 19 For Review Only 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 Figure 2. Image from Williams Whyte’s movie the “Social Life of Small Urban Spaces” Whyte used video and human observation to measure 42 public space he believed seating was essential to the function of good urban space and believed developing movable seating helped to create new 43 1 44 spaces to socialize within the public realm. 45 46 47 “Space syntax” is also a well known theory used to understand the relationship between the 48 49 society and space through examining the configuration of space (Hillier et al., 1976, Hillier and 50 51 Hanson, 1984, Hillier, 2007). Space syntax, which began as a theoretical framework in the 1970s, has 52 53 become a set of computer based analytical tools that “represent, quantify, and interpret” the “ordering of 54 55 56 1 http: //secretagent.com.au/overlooked-element-urban-design-places-sit/ 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Page 7 of 39 Environment and Planning B: Urban Analytics and City Science 7 1 2 3 space into relational systems embodying social purposes” (Figure 3) (Hillier and Hanson, 1984:262). 4 5 Rather than focusing on the “surface properties,” such as appearance and style, space syntax is based on 6 7 the assumption that the spatial organization of societies follows “morphic languages,” a set of rules 8 9 10 utilizing a set of elementary objects can be combined (Hillier et al., 1976). The syntactic theory argues 11 12 this morphic language realizes the social through the syntax in the real world (Hillier et al., 1976: 153). 13 14 Analysis tools associated with space syntax have been used to evaluate design in transportation studies 15 16 (Giannopoulou et al., 2012; Kaparias et al., 2015; Omer and Kaplan, 2017; Turner, 2007; van der Hoeven 17 18 and van Nes, 2014), historical analysis of the morphology of cities (Alitajer and Molavi Nojoumi, n.d.; 19 For Review Only 20 Alkamali et al., 2017; Hanson, 1989, 2000), and crime analysis (Wu et al., 2015). Some studies have used 21 22 space syntax to model and analyze pedestrian movements and its relationship to the streets or street lights 23 24 (Bozorg Chenani et al., 2016; Choi et al., 2006; Xiana and Lipeng, 2017). As with many theories that boil 25 26 the principles of design into a set of rules, the work has been criticized for being too rational and not 27 28 measuring the subjective aspects of public space. 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 Figure 3. Space Syntax visibility graph analysis. One of the many variables Hillier found essential to public space was sight lines, and he 49 developed techniques to measure this. 50 51 52 Much of the work to measure public space has been toward the results of determining Urban Design 53 54 guidelines. In People Places, Marcus and Francis (1997) developed practical recommendations for the 55 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Environment and Planning B: Urban Analytics and City Science Page 8 of 39 8 1 2 3 design of different types of public spaces based on the fieldwork conducted by students at the University 4 5 of California, Berkeley. Their observations of uses and activities in these places contained quantitative 6 7 counts of the number of pedestrians and the percentage of people sitting and also included qualitative 8 9 interviews to understand what people liked about the spaces, their recommendations for modifications to 10 11 12 the space and the reasons why they used the urban space. For instance, the analysis of AP Giannini Plaza 13 14 in San Francisco’s Financial District attested to the importance of sunlight and diversity of seating options 15 16 as more activities were observed in the sunny south side of the plaza rather than the central plaza, which 17 18 lacks programs and adequate sunlight (Figure 4) (Marcus and Francis, 1997: 55). These studies 19 For Review Only 20 demonstrate one can derive design guidelines for public space by carefully examining the spatial elements 21 22 of a space and how individuals behave or act in that space. 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Page 9 of 39 Environment and Planning B: Urban Analytics and City Science 9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 For Review Only 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 Figure 4. Images from People Places (Marcus, 1990) 45 46 47 Data and Sensors for Digitally Measuring Public Life 48 49 50 Researchers increasingly measure a variety of elements in cities through the data generated by a range of 51 52 technologies including image and video surveillance, location based technologies that access GPS units, 53 54 Wi Fi sniffing, Bluetooth tracking, cellular networks, and 3D range sensors (Ben Joseph, 2011; Kitchin, 55 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Environment and Planning B: Urban Analytics and City Science Page 10 of 39 10 1 2 3 2013; Williams, 2016). One of the ways we currently measure pubic space is through image analysis, 4 5 which uses machine learning techniques to determine flows of humans and vehicles. As early as 1994, 6 7 Koller et al. analyzed data from surveillance cameras to determine “vehicle features, such as vehicle type, 8 9 turn signals and brake lights” (Koller et al., 1994: 130). As technology improved, researchers such as 10 11 12 Stauffer and Grimson (1999) were able to conduct similar analyses over time that determined not only 13 14 what model a car was in a given frame but how regular traffic patterns emerge and change when there is 15 16 an anomaly such as an accident. Improvements in artificial intelligence and machine learning have 17 18 furthered since these early works, and now toll roads across the world can use automatic license plate 19 For Review Only 20 recognition (ALPR) software to algorithmically analyze license plate numbers to bill drivers, charge for 21 22 speeding tickets, and even manage traffic. Some notable examples of this include managing the 23 24 congestion pricing zones of London (Litman, 2005) and ensuring lawful behavior in Beijing (Norris et al., 25 26 2002). Although image analysis was one of the earliest techniques used to quantify public space, 27 28 ubiquitous implementation is difficult as the infrastructure needed to manage it can be costly across an 29 30 31 entire city. 32 33 34 As a result, researchers have looked for less expensive hardware to conduct similar analyses. For 35 36 example, 3D range sensors such as the Microsoft Kinect have been repurposed for the measurement of 37 38 public space. The Kinect was released in 2012 and developed primarily for video game interaction. 39 40 However, immediately after its release, researchers began to investigate whether Kinect technology can 41 42 produce highly accurate mappings of pedestrians in space. Seer, Brändle, and Ratti studied the narrow 43 44 hallway on MIT’s campus known as the Infinite Corridor, and they were some of the first researchers to 45 46 reproduce results in the realm of modeling pedestrian movements and behavior traditionally conducted by 47 48 analyzing video feeds (2014). Later, Brščić et al. (2013) pushed the idea further by using Kinects to study 49 50 51 larger public spaces; they described how they used Kinects to visualize traffic patterns within a large mall 52 53 in Osaka. The sensitivity of Kinects made it possible for researchers not only to detect broad pedestrian 54 55 patterns but also investigate what an individual was doing in a given public space. Popa, Rothkrantz, et al. 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Page 11 of 39 Environment and Planning B: Urban Analytics and City Science 11 1 2 3 (2013) used Kinects to detect when a person might be engaged in one of several types of shopping 4 5 behavior including picking up an item, checking the price tag, trying it on in a mirror, and putting it back 6 7 in the display. One of the criticisms for using Kinect for measuring public space is they do not work well 8 9 outside as there is a lot of interference and necessary post processing . 10 11 12 13 Although Kinects are sensitive and useful for hyperlocal data capture, the main drawback is their sensing 14 15 range is short because they can only capture data within their field of view. GPS units, however, can track 16 17 anywhere in the world, as their field of view is global in scale. Since the mid 1990s, researchers have 18 19 used GPS sensors to understandFor vehicular Review traffic patterns (D’Este Only et al., 1999; Zito et al., 1995). It is only 20 21 within the past decade that GPS has been applied more intensively to study pedestrian behavior. In 2005, 22 23 Asakura and Iryo (2005) used GPS receivers to track tourist behavior in Kobe. Similarly, Harder et al. 24 25 (2008) provided GPS units to people visiting specific parks in the Netherlands to ascertain the feasibility 26 27 of tracking park usage patterns. Until the advent of smartphones, however, GPS tracking was limited to 28 29 researchers giving participants individual GPS units, limiting the number of potential subjects, size of the 30 31 32 potential study area, and duration of the study. 33 34 35 Smartphones have increased the scale at which we can perform digital sensing and the speed with which 36 37 we can conduct these analyses. Researchers can now draw on the different technologies embedded in the 38 39 phones, including GPS, Wi Fi, and Bluetooth, as well as cellular network connections when users place a 40 41 call or send an SMS. Williams and Currid (2014) used the GPS capabilities of smartphones devices to 42 43 track 100 fashion designers for two weeks to understand how they used manufacturing services in New 44 45 York City. Their research illuminated the fact that the Garment District was used more by large scale 46 47 designers than small scale designers, revealing that removal of the district could have a more significant 48 49 impact of the economy of the city as it would affect the work processes of some of the biggest players in 50 51 52 the fashion industry. 53 54 55 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Environment and Planning B: Urban Analytics and City Science Page 12 of 39 12 1 2 3 Similarly, the ubiquity of cell phones has resulted in rich datasets that help researchers understand the 4 5 relationship between people and their social lives as situated in space and time. One of the first studies to 6 7 look at this was the Mobile Landscape project developed by the Senseable City Lab. In this project, 8 9 researchers visualized people’s locations and spatial trajectories by tracking mobile phones and other 10 11 12 location based services across in Milan, Italy and the city of Graz, Austria(Ratti et al., 2006) (Ratti et al., 13 14 2007). Similarly, researchers have used the technologies embedded in smartphones to track people at the 15 16 Louvre to understand how the paths taken and art seen by people who rush through the museum differ 17 18 from those who spend longer in the galleries (Yuji Yoshimura et al., 2014). Since these early studies, cell 19 For Review Only 20 phone data records (CDR) have been used for everything from managing traffic flows to tracking Ebola in 21 22 Africa. The potential for use to analyze this data in public space is exponential, yet it has been hard to 23 24 acquire this data to make small scale observations. 25 26 27 Most cities do not rely on smartphone generated data because it is hard to obtain and, as a result, 28 29 have created their own sensor networks. These networks can have costly outlay and are currently in use 30 31 32 only in a handful of cities such as Paris, Melbourne, Rio de Janeiro, New York, and Chicago. These 33 34 sensors measure a diverse group of elements in the urban environment. In Chicago, the Array of Things 35 36 (IoT) sensors measure “temperature, barometric pressure, light, vibration, carbon monoxide, nitrogen 37 38 dioxide, sulfur dioxide, ozone, ambient sound intensity, pedestrian and vehicle traffic, and surface 39 40 temperature” (Array of Things, n.d.; Zanella et al., 2014). Similarly, Paris’s collaboration with Cisco 41 42 collects data on traffic, air quality, and noise pollution in real time and publishes it directly onto the city’s 43 44 open data portal (Reichert, n.d.). The data can be visualized on site on two interactive screens in public 45 46 spaces to provide a clear image of what is taking place. 47 48 49 Because of the difficulty of deploying these large scale systems, global corporations such as 50 51 52 Cisco, IBM, and Google are seeking to benefit from developing these sensing technologies for cities. 53 54 IBM worked with Rio to develop perhaps the most extensive public custom sensing network, one that 55 56 operates as “a citywide system integrating data from some 30 agencies, all under a single roof” (Singer, 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Page 13 of 39 Environment and Planning B: Urban Analytics and City Science 13 1 2 3 2012). Similarly, Alphabet, Google’s parent company, operates a “Smart City” division named Sidewalk 4 5 Labs. Recently the organization has received press coverage on its work with Toronto as well as its spin 6 7 offs, Coord and Cityblock, which are its transit specific and public health networks, respectively (Sauter, 8 9 2018). On the other side of the globe, the City of Melbourne has implemented a 24 hour pedestrian 10 11 12 counting system with 44 sensors that measure pedestrian activity at strategic locations throughout the city 13 14 each day. The data collected reflects pedestrian activity, which serves as a good proxy for walkability of 15 16 the space and is a direct reflection of measuring cities’ livability and vibrancy (Doan et al., 2015). 17 18 19 Do-It-Yourself Sensing For Review Only 20 21 22 One criticism people express related to sensor networks as digital research techniques is as they 23 24 become more pervasive, it is unclear who will own and have access to the data. Projects developed by 25 26 Google, Cisco, and IBM usually work at the highest levels of government and getting that data into the 27 28 hands of citizens in a form that useful for the scale of a small urban park is not always easy. However, 29 30 with the development of open source, or DIY, technologies and the emergence of a robust crowdsourcing 31 32 33 culture, projects increasingly enable engaged citizens to work with new tools to digitally measure 34 35 everything from air quality to pedestrian traffic. Because these tools are often developed by the same 36 37 people who are interested in using the data, it is much easier to use the data for urban analytics. 38 39 40 To some, DIY implies subterfuge and counterculture whereby people who undertake DIY 41 42 projects to assert their independence and empowerment by participating outside the formal channels of 43 44 innovation (Wolf and McQuitty, 2011). Civic DIY projects, often termed “Do it Yourself Urbanism,” are 45 46 largely based on the belief that almost anyone can make and implement flexible, incremental changes to a 47 48 city’s urban design (Iveson, 2013). While DIY can often mean doing something outside government 49 50 channels, the government sometimes participates in these projects. Perhaps the most notable examples of 51 52 53 DIY technology are those used to collect and analyze urban environmental factors such as air quality, 54 55 particulate matter, and noise pollution. “Smart Citizen” is a physical sensor toolkit with a corresponding 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Environment and Planning B: Urban Analytics and City Science Page 14 of 39 14 1 2 3 digital platform that “connects people with their environment and their city to create more effective and 4 5 optimized relationships between resources, technology, communities, services, and events in the urban 6 7 environment” (Smart Citizen, n.d.). The toolkit comes in an enclosed box containing a data processing 8 9 board as well as “sensors that measure air composition (CO and NO2), temperature, humidity, light 10 11 12 intensity and sound levels.” (Figure 5) All the information related to its design is open source, and people 13 14 are free to modify them for different purposes. 15 16 17 Another DIY environmental sensing project, developed by a Canadian organization named 18 19 SensorUp, is “The Smart CitiesFor Starter Review Kit.” The project combines Only low cost sensors for people to use in 20 21 their neighborhoods to produce a crowdsourced online map of air quality. The kit has three sensors— 22 23 temperature, humidity, and PM 2.5—to measure air quality. With Calgary as a pilot city, SensorUp is 24 25 planning to hold a series of workshops in ten different cities to build a network of volunteers who will be 26 27 responsible for deploying the kits (SensorUp, n.d.). As the citizen sensing movement gains traction, city 28 29 officials around the world are interested in adopting its methods. In early 2014, officials in Amsterdam 30 31 32 provided over 70 citizens with Smart Citizen kits and instructions on how to use them in their 33 34 neighborhoods (Henriquez et al., n.d.). The experiment succeeded in involving a relatively large group of 35 36 active citizens in Amsterdam and raised questions regarding validation and analysis of the data as well as 37 38 the technology used in the sensor kits. Following the experiment, official measuring organizations in 39 40 Amsterdam actively participated in the discussions and voiced their ambition to be involved in future 41 42 citizen measuring networks. 43 44 45 Along with the environmental sensing projects, several projects in the market set out to provide 46 47 customizable sensor toolkits that measure urban spaces through image analysis. Placemeter, which uses 48 49 computer vision technology to automatically measure pedestrian and vehicular traffic, was promoted 50 51 52 during its early stages as the “only plug and play urban measurement tool on the market” that could be 53 54 used by anyone without any coding knowledge (Placemeter, 2018). In addition to the camera toolkit, 55 56 which can be bought from Placemeter’s website, citizens could use personal webcams or conventional 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Page 15 of 39 Environment and Planning B: Urban Analytics and City Science 15 1 2 3 security cameras to count passersby without recording the video. Placemeter attempted to scale up and 4 5 integrate situated cameras across New York City into its network and use their live video camera feeds to 6 7 calculate vehicle and pedestrian traffic levels in real time(One Company Is Trying To Count And Track 8 9 10 All Of New York City’s Pedest, n.d.). This did not happen as after being purchased by Netgear, an urban 11 12 sensing company, the product was retired in 2016. 13 14 15 16 17 18 19 For Review Only 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 Figure 5. Smart Citizen Kit. Smart Citizen. 2017. Digital Image. Available from: Smart Citizen, https: //www.smartcitizen.me (accessed February 44 20, 2018). 45 46 47 DIY sensing technologies provide users with instant feedback and access to data on their 48 49 environment that can be used to argue for policy. While much of the work with this technology has been 50 51 environmental, it can easily be applied to measure public space. With both the public and private sectors 52 53 incrementally embracing citizen sensing, new models to measure and urban environments are emerging. 54 55 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Environment and Planning B: Urban Analytics and City Science Page 16 of 39 16 1 2 3 Furthermore, as sensor technologies became more technically and financially accessible, it is possible for 4 5 DIY sensing kits to complement formal and complex sensor networks. 6 7 8 9 Tactical Urbanism 10 11 12 DIY does not only apply to sensor technologies; it also often refers to a trend to develop low cost and 13 14 temporary urban planning projects that can play a pivotal role in experimenting with public space. 15 16 Sometimes referred as DIY, tactical, or pop up urbanism, this new model integrates measurement and 17 18 prototyping tools in the early stages urban projects. Because these projects are often used to test urban 19 For Review Only 20 design, it is important to measure how well they work, much of which currently happens through human 21 22 observations. One New York City’s most successful tactical urbanism projects, The Green Light for 23 24 Midtown, developed by the New York City Department of Transportation (NYCDOT)’s aimed to 25 26 improve the pedestrian and cyclist environment and to reduce congestion in Midtown Manhattan (The 27 28 29 New York City Department of Transportation, 2010). The demonstration closed several streets along 30 31 Broadway to create a pedestrian plaza. According to the evaluation report of the project, an extensive 32 33 manual data collection was conducted to record the increase in pedestrian volumes. Along with the 34 35 pedestrian counts, pedestrian behavior (e.g., sitting, eating, talking) was also analyzed by volunteer 36 37 observers who visited the site daily. The results showed stationary activity increased by 84%, and visitors 38 39 to the areas were 74% satisfied with the improvements. This data analysis provided evidence to support 40 41 permanently closing this area to cars in 2010 (The New York City Department of Transportation, 2010). 42 43 44 The Philadelphia Parklet Program is another tactical urbanism project in which volunteers 45 46 generated data through the observation of a temporary installation. In 2011, University City District 47 48 49 (UCD), working with the City of Philadelphia, installed the city’s first parklets—a small public space 50 51 resulting from converting parking space into a more human friendly, relaxing environment. For the 52 53 measurement process, a UCD intern monitored the parklets for the full duration of the business hours of 54 55 the host business(University City District, n.d.: 11). The intern recorded “the arrival and departure time of 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Page 17 of 39 Environment and Planning B: Urban Analytics and City Science 17 1 2 3 each user, their gender, and approximate age, whether or not they were a patron of the adjacent business, 4 5 and the activities in which they participated (eating, talking, etc.).” The results of these surveys showed 6 7 the parklets increased revenues of shops around it by an average of 20%. In addition to the increase in 8 9 revenues, the city staff made their case for more parklets by showing the public activity will occur in a 10 11 12 space usually hosting one or two parked cars. The study also showed roughly 20% 30% of all users were 13 14 non patrons, which meant parklets served as a public space for passersby. The success of the pilot 15 16 demonstration of the parklets led to the consistent implementation of parklets in following years. As of 17 18 2017, UCD has six active parklets around the city. 19 For Review Only 20 21 In data collected in the Philadelphia and New York, tactical urbanism projects helped to change 22 23 urban design policy, showing the importance of measuring the success of these projects. As short term 24 25 planning projects gain more attraction, sensors that automatically record the metrics associated with these 26 27 projects can help cities develop public spaces that are significantly more activated and human centered. 28 29 30 31 Interactive Furniture 32 33 34 Advancements in sensing technology and digital tools have allowed for the emergence of smart urban 35 36 furniture projects that can help us better understand how humans experience and interact with the city 37 38 (Frenchman and Rojas, 2006). Often embedded with sensors, digital facades, or moveable physical 39 40 elements, this new generation of interactive urban furniture responds to human interaction and 41 42 environmental factors. One notable project is the Soofa bench, which is embedded with sensors to 43 44 measure pedestrian traffic and activity in outdoor public spaces (Pribyl et al., 2017). Soofa has partnered 45 46 with municipal governments including Las Cruces, NM, to enable city leaders and decision makers to 47 48 49 quantitatively evaluate the ideal locations for free public WiFi. They built these recommendations based 50 51 on the data gathered by Soofa on the use of spaces around the city (Soofa, 2017). Soofa gathered the data 52 53 by counting the number of phones that connected to their Wi Fi as users passed by, giving them a 54 55 pedestrian count. Another well publicized example of interactive furniture is the network of LinkNYC 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Environment and Planning B: Urban Analytics and City Science Page 18 of 39 18 1 2 3 stations located across New York City where public pay phones once stood. These stations provide free 4 5 Wi Fi, free phone calls, device charging, and an informational/advertising billboard. Data is collected on 6 7 the device and provided in an anonymized form and can be used to understand pedestrian usage of public 8 9 10 space and service (Sinky et al., 2017). This enables governments to have more influence on the 11 12 management of the cities by building profiles of specific locations that are valuable for resources 13 14 allocation and informing decisions on parking, lane changing, and traffic enforcement (Heath, 2016). 15 16 CityTree is a combined bench and vertical moss covered wall designed for Hamburg that monitors 17 18 environmental conditions in public space through sensors embedded in the wall (Boissevain, 2018). 19 For Review Only 20 (Figure 6). Another project San Diego is evaluating for city wide implementation uses “smart” sensor 21 22 laden street lights that help residents to find vacant parking spaces and the police to locate the source of 23 24 gunfire (Álvarez et al., 2017). 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
49 50 51 Figure 6. Green City Solution. CityTree. 2017. Digital Image. Available from: TreeHugger, https: //www.treehugger.com/sustainable product 52 53 design/citytree purifies polluted air green city solutions.html (accessed February 22, 2018). 54 55 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Page 19 of 39 Environment and Planning B: Urban Analytics and City Science 19 1 2 3 As interactive urban furniture becomes a popular medium for real time data collection, it opens a 4 5 variety of new possibilities for measuring and understanding public spaces. Despite the growing adoption 6 7 of sensing urban furniture, the data is often hard to access for analysis of small urban spaces because it is 8 9 often part of more extensive top down civic technology projects. This is true for the LinkNYC kiosks, 10 11 12 where the data has been made available online to the public, but because much of it is aggregated at the 13 14 scale of the city, it is hard to use it to measure small public spaces such as the ones developed in tactical 15 16 urbanism projects. Researchers often turn to using human observation to measures the effectiveness of 17 18 these spaces, but what if “smart” urban furniture was more mobile and could be deployed by those 19 For Review Only 20 developing these temporary urban spaces to measure how well they are used? Making a system that easily 21 22 gathers urban sensing data and provides it as open sourced in a standardized format could help us 23 24 digitally measure public life. 25 26 27 Methods Building a DIY Sensing Project the Measure Public Life 28 29 30 31 Determining the Public Life Data to Collect 32 33 34 Benchmark set out to test whether temporary urbanism projects could be evaluated using DIY 35 36 sensor embedded urban furniture. Instead of making up new standards for how to measure public space, 37 38 we used the methods developed by Jan Gehl. Specifically, Benchmark looked at criteria in the Public 39 40 Life Diversity Toolkit published in 2016 by the Gehl Institute, which seeks to quantify how public space 41 42 can influence and encourage social interactions as well as understand what conditions of public space 43 44 promote more social behavior. The Public Life Diversity Toolkit provides a method for observing and 45 46 classifying a broad range of stationary interactions in public space into a matrix with over 20 categories of 47 48 49 types of interactions. Stationary interactions refer to activities people engage by staying at a location over 50 51 5 minutes. The Toolkit, which is made up of a series of questionnaires, identifies each type of interaction 52 53 according to an individual’s body position (e.g., standing, sitting, laying down) and their activity (e.g., 54 55 being, waiting for transit, commercial, cultural, physical, children playing). Leveraging sensing 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Environment and Planning B: Urban Analytics and City Science Page 20 of 39 20 1 2 3 technology, the project aimed to automate some of the observations by using algorithmic image 4 5 recognition on the photos generated by GoPros and well as sensors embedded in the bench. 6 7 8 9 10 11 12 13 14 15 16 17 18 19 For Review Only 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 Figure7. Taken from the public Life Data Protocol produced by Gehl helps the illustrate the many questions Gehl recommends be asked to 39 40 understand issues around public life. Our work focused on activities that could be in included in the OBJECTS, GEOTAG, and GROUPS section 41 above. 42 43 44 The Gehl method defines “stationary interactions” as those when people stay in one area for over 5 45 46 minutes; “pedestrian interactions” refer to cases when people stop for a period shorter than 5 minutes and 47 48 express their curiosity in the benches. Our research investigates stationary interactions by asking three 49 50 questions: Are people sitting on the urban furniture we developed; are people moving the benches around; 51 52 and are the benches clustered spatially? In this part, we aimed to understand if sensing technology can be 53 54 55 used to detect whether people were engaging in seated stationary interactions and whether they were 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Page 21 of 39 Environment and Planning B: Urban Analytics and City Science 21 1 2 3 doing this in groups or alone. In addition to stationary interactions, we also sought to understand 4 5 pedestrian interactions. To understand this measurement, we asked: How many people pass by the site? 6 7 Are people interested in the benches? 8 9 10 11 Design of Furniture and Sensors 12 13 14 Benchmark uses modular, movable furniture units as the key element in studying human 15 16 interaction and movement within a public space. The design of modular furniture set out to ensure 17 18 replicability, modularity, durability, scalability, and ability to capture the measurements needed. This led 19 For Review Only 20 to the development of two pieces of furniture—the Bench, embedded with a sensors box, and a sandwich 21 22 board, for signage and games, which housed a GoPro. The bench design is lightweight, allowing visitors 23 24 to move them without difficulty. Inexpensive materials were used to facilitate the mass production and 25 26 implementation of the benches. The design can be downloaded, taken to a makerspace, and cut from 27 28 29 plywood using a CNC router (Figure 8). The benches stack for quick storage and connect easily with one 30 31 another to encourage playfulness on site. 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Environment and Planning B: Urban Analytics and City Science Page 22 of 39 22 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 For Review Only 20 21 22 23 24 25 26 27 28 29 30 31 32 33 Figure 8: Taken from the Benchmark report, CNC files are provided so that anyone can take the files to a CNC router to be cut. 34 35 Sensor Development 36 37 38 39 We conducted an extensive review of available sensors and combined a number of devices in a 40 41 box for installation in the bench. The sensors included in the transparent weatherproof plastic box were 42 43 load cell sensors to detect the presence or absence of a person on a bench, GPS, accelerometers, and 44 45 gyroscopes to detect changes in orientation and bench movement. Decibel level sensors measured 46 47 whether people were conversing on or near the benches. Digital luminosity sensors quantified the ambient 48 49 light levels around the benches. The sensor box is clear so visitors to the site would know they were being 50 51 tracked so the project is transparent (Figure 9). Cell phone SIM cards sent data back to the project’s 52 53 servers. The GoPros housed in the Sandwich board took images that were processed using machine 54 55 learning tools. 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Page 23 of 39 Environment and Planning B: Urban Analytics and City Science 23 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 For Review Only 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 Figure 9. Image of the design of the sensor box, which was included in the DIY report so that others can replicate the design. 36 37 38 Experiments: Benchmark in Practice 39 40 41 To test the viability of the system as a placemaking tool, we implemented the system three 42 43 times—first at MIT to make it easy to fix design problems, then as part of a Build a Better Block project 44 45 46 in Charlotte, NC, and finally at an event at Boston’s City Hall. The project on MIT’s campus was 47 48 deployed the between July 25 and August 6, 2017, from 10: 00 a.m. to 6: 00 p.m. daily. We selected the 49 50 North courtyard because it is located at the intersection of several busy pedestrian corridors, ensuring a 51 2 52 variety of pedestrian activity and possibilities for public life interactions. Signage attached to the 53 54 55 2 Additionally, it is close to the research lab’s MIT office, which made it easier to manage, store, and perform immediate technology repairs. The 56 team checked on the benches frequently to ensure the data collection process was proceeding smoothly and to take on site observations. The 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Environment and Planning B: Urban Analytics and City Science Page 24 of 39 24 1 2 3 sandwich boards notified people passing through the site that we were collecting data and assured them 4 5 no personally identifiable information would be stored. Visitors to the site were playful with the benches 6 7 (Figure 10). Many people used the benches as a backrest or a table. The first week of the test was used to 8 9 work out issues with the sensors and their communication with the server; therefore, much of the data we 10 11 12 collected comes from the second implementation week at MIT. 13 14 15 In September 2017, the Benches were a part of Better Block Belmont, a community event aimed 16 17 at revitalizing a neighborhood in Charlotte, NC. Four Benchmark benches and a sandwich board were 18 19 shipped to the event, and theFor Better Block Review team downloaded Only the data after the event themselves, showing 20 21 the project was replicable for those outside the development team. Not only did they succeed in setting up 22 23 the system but the organizers saw the benches as a critical factor in invigorating their constituents’ 24 25 participation in the weekend long program. In October 2017, two Benchmark benches and the sandwich 26 27 board were deployed at Hubweek, a weeklong festival of civic innovation in Boston’s City Hall Plaza. 28 29 Based on the previous two releases of the benches, the code was enhanced to interpret the data coming 30 31 32 from the sensor and was able to get more precise measurements than the other two previous releases. 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Page 25 of 39 Environment and Planning B: Urban Analytics and City Science 25 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 For Review Only 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 Figure 10. Image of the release of the benches on MIT’s North court August 2017. 36 37 38 I. Data Analysis 39 40 41 Although the sensors embedded in the benches provided data, the analysis conducted from the visual 42 43 analytics and GoPro imagery was more accurate. The GoPros were used to detect people and track how 44 45 they used the space. We used a state of the art object detection algorithm based on Convolutional Neural 46 47 Networks (Krizhevsky et al., 2012) to process the images. Convolutional Neural Networks (CNNs), 48 49 50 inspired by the structure and arrangement of object recognition pathways in primates’ brains, can make 51 52 their own deductions about whether an object is present in an image by incorporating elements of local 53 54 context (e.g., pixels in images). In general, researchers must train CNNs with more than 10,000 images to 55 56 construct robust image recognition models. In this research, we used Faster R CNN: Regions with CNN 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Environment and Planning B: Urban Analytics and City Science Page 26 of 39 26 1 2 3 features (Ren et al., 2015), a pre trained model that already had pedestrian data. The main difference 4 5 between a CNN and the Faster R CNN method is the latter is calibrated to detect multiple objects and 6 7 their boundaries in an image. the Faster R CNN required too large a training dataset for a relatively small 8 9 number of bench images we had, so we turned to a new neural network named “You Only Look Once" 10 11 12 (YOLO) (Redmon et al., 2015). The detection performance we got from the YOLO network was 13 14 promising given that YOLO method helps us to considerably increase the mean average precision (mAP) 15 16 with less data compared to other methods. Furthermore, the YOLO method proved to be suitable to be 17 18 deployed to the various mobile devices (e.g., Raspberry Pi) because YOLO can process images much 19 For Review Only 20 more quickly than the Faster R CNN algorithm when comparing the number of frames processed per 21 22 second (FPS). This allowed us to reliably compute necessary predictions in the field without storing the 23 24 images in a temporary database. These predictions could then calculate the number of pedestrians visiting 25 26 the site and compute various indices for measuring public interaction. 27 28 29 30 Detectors Pros Cons 31 32 33 34 Haar Cascade Classifiers Robust when the task requires Hard to identify people when 35 (Viola and Jones, 2001) detecting human faces. their face is not visible. 36 Harder to detect people when 37 they are distant from the camera. 38 39 SVM HOG Fast and robust when people are Harder to detect people distant 40 (Dalal and Triggs, 2005) at certain distances from camera. from the camera. 41 Low recall when multiple people 42 are present in the image. 43 44 Faster R CNN VGG 16 Highest mean average precision Harder to train custom classes 45 (Ren et al., 2015) (mAP) among all implemented with few examples. 46 methods. Slow processing time during 47 prediction. 48 49 “You Only Look Once” Easy to train with new object Lower mAP (mean average 50 Network (YOLO) classes.Faster processing time precision) compared to Faster R 51 52 (Redmon et al., 2015) during prediction. Better fit for CNN VGG 16 algorithm. 53 mobile use. (e.g., can run 54 efficiently on a Raspberry Pi) 55 Figure 11. Compares the different machine learning algorithms we used to analyze the data. 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Page 27 of 39 Environment and Planning B: Urban Analytics and City Science 27 1 2 3 4 5 6 In addition to using the images to determine the presence of pedestrians on our site, the GoPro 7 8 images also turned out to be an invaluable resource validating the accuracy of the data collected from 9 10 each bench. Researchers validated the pressure sensor data and the outputs of the machine learning 11 12 algorithms by watching the videos made from a series of GoPro images taken in two second intervals. 13 14 Analysis of the data showed the GPS units produced inconsistent data, which made it hard to reliably 15 16 detect the movement of the benches. The GPS accuracy was sometimes off by up to 300 feet due to 17 18 atmospheric conditions and interruptions of satellite signals by nearby buildings. The results using 19 For Review Only 20 analysis based on the GoPro images to determine the location of the benches was far more accurate than 21 22 23 the GPS readings. 24 25 26 Results 27 28 29 Determining Stationary Interactions 30 31 32 As noted previously, the study was interested in measuring stationary interactions as defined by 33 34 Gehl Public Life Diversity Toolkit. The work focused on determining if people were sitting on the 35 36 benches, if people were moving the benches around, and if the benches clustered spatially. Analysis of the 37 38 39 data showed the pressure sensor included in the bench was effective in measuring whether people were 40 41 sitting on the benches. However, the GPS sensors included in the sensor box were unreliable for detecting 42 43 the location of the bench. Therefore, analysis of the GoPro images was used to determine the location of 44 45 the benches. 46 47 48 To understand the extent to which social interactions took place around the benches, data taken 49 50 from the GoPros was used to identify when benches clustered with one another. A cluster of benches is 51 52 when two or more benches are close enough to facilitate social interaction, estimated to be 1.5m. To 53 54 automate cluster measurements, an algorithm was developed to calculate the distance between every 55 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Environment and Planning B: Urban Analytics and City Science Page 28 of 39 28 1 2 3 possible pair of benches when the location of any of bench changed. The results from these measurements 4 5 were then used to determine the proportion of the day each bench was clustered and solitary (Figure 12). 6 7 Across the five day analysis period at MIT, four out of the six benches were part of a cluster for over 60% 8 9 10 of the time, which suggests people strongly prefer street furniture that allows them to sit with others. 11 12 Manual analysis of the GoPro images later verified the accuracy of the algorithm. 13 14 15 16 17 18 19 For Review Only 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 Figure 12.. Sitting Interaction Analysis on Data Collected on August 6, 2017. 2017. 40 41 42 Combining the results of the cluster analysis with the seating analysis determined from the 43 44 pressure sensor allowed for the measurement of social interaction. By determining the distance between 45 46 benches and whether people were sitting on them, we could identify if there was social interaction. This 47 48 allowed for the development of a data set of social interaction. (See Figure XXX). In our study, it was 49 50 common for two clusters of benches to develop under the shade of nearby trees, and the benches were 51 52 used most often around the lunch hour and directly after work. 53 54 55 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Page 29 of 39 Environment and Planning B: Urban Analytics and City Science 29 1 2 3 Pedestrian Interactions 4 5 6 The research also focused on measuring pedestrian interaction—people who stay in a place for 7 8 9 fewer than 5 minutes (Gehl, 2015). People passing through the courtyard often stopped to look at the 10 11 benches, and because of these fleeting expressions of interest, they stayed in the plaza longer and often 12 13 interacted with the benches in a stationary activity. To understand pedestrian interactions as a category, it 14 15 is necessary to differentiate people who are passing through the site from those who stayed and expressed 16 17 curiosity. If people stopped while moving through the courtyard for more than 6 seconds, it was marked 18 19 as a curiosity interaction or For“Curiosity Review Index.” Only 20 21 22 The number of people present on the site was computed with the R CNN algorithm. The GoPro 23 24 images were captured every 2 seconds, and we could track how each person’s bounding box moves 25 26 through successive frames. Then we used an algorithm to identify the length for which each bounding box 27 28 29 appeared to differentiate people who merely passed through the site from those who stayed for more than 30 31 6 seconds, the latitude and longitude coordinates were calculated for each person as they moved 32 33 throughout the courtyard based on triangle similarity. We aggregated these results for each day and 34 35 visualized the entire dataset as a heat map (See Figure 13). The algorithm detected individuals who 36 37 walked by our site with near perfect accuracy, but there were a few shortfalls. First, the algorithm 38 39 performed better with individuals and smaller groups in the image frame, while the accuracy decreased 40 41 when there were more than eight people. Second, although the algorithm could accurately identify people 42 43 standing and walking by, it was not reliable to detect people when they were sitting on benches. 44 45 Therefore, the pressure sensor data was used to determine when people were sitting on the benches. 46 47 48 Finally, the algorithm struggled to recognize people who were farther away from the camera it saw them 49 50 as only a few pixels in the image, which made it hard for R CNN to identify. Despite the limitations, the 51 52 research team could compute the location of pedestrians to identify how the pedestrian movement 53 54 changes over time during the data collection period. Figure 13 is a heat map using all pedestrian locations 55 56 aggregated by day and demonstrates different patterns emerge during different days of observation. 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Environment and Planning B: Urban Analytics and City Science Page 30 of 39 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 For Review Only 20 21 22 Figure 13. Pedestrian Heatmap Analysis. 2017. 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 Figure 14. Space Traffic Analysis. 2017. 41 42 43 The Gehl method defines a place’s “stickiness” as the number of people who choose to stay and 44 45 engage in stationary interactions out of the total number of people passing by. Our Curiosity Index serves 46 47 as an analytic bridge to identify the transition people’s behavior from passerby to stationary. Figure 14 & 48 49 15 shows there are more people who were simply curious than people who transitioned to sitting on the 50 51 Bench. The results of the analysis showed the site was extremely active, with large numbers of 52 53 pedestrians and people interested in engaging in the during the lunch hour and mid afternoon. The site 54 55 was much less active after the workday. 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Page 31 of 39 Environment and Planning B: Urban Analytics and City Science 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 For Review Only 20 21 22 23 24 25 26 27 Figure 15. Pedestrian Interaction Analysis on Data Collected on August 4, 2017. 2017. 28 29 30 DISCUSSION / CONCLUSION 31 32 33 Recognizing that public realms function differently across countries, cultures, and even cities, the 34 35 Benchmark project developed a tool anyone around the world can augment, iterate, and customize to 36 37 38 measure public space. The furniture, sensors, and software are all DIY, allowing anyone to replicate them. 39 40 Perhaps one of the biggest findings was that the machine learning algorithms on the GoPro images were 41 42 much more efficient and accurate at measuring public space use then sensors embedded in the bench 43 44 themselves. The algorithms used to measure the Benchmark prototype were developed to understand and 45 46 detect movements in images. Therefore, it is not surprising they allowed for multiple measurements of the 47 48 interactions on site during the three tests. The Benches were easily replicated in two additional sites 49 50 outside the tests at MIT, realizing the intent to make these a toolkit that allows anyone to access the data 51 52 and measure urban design. 53 54 55 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Environment and Planning B: Urban Analytics and City Science Page 32 of 39 32 1 2 3 The Benchmark project established a robust workflow to assist human observation with sensors 4 5 and machine learning techniques to understand social interactions within public spaces. From the 6 7 technical point of view, the research tested various sensors to capture the human movement in space as 8 9 well as the interactions with the street furniture and analyzed the tradeoff between accuracy and flexibility 10 11 12 among different machine learning algorithms. By freeing up human labor from taking notes on the 13 14 locations, duration, proximity, and other factors of social interactions easily gathered from digitally 15 16 enhanced street furniture, Benchmark allows observers instead to focus more on the nature of the 17 18 interactions taking place in the public space. The data collected was used to evaluate the conditions that 19 For Review Only 20 promote interactions and vitality in the observed space. The unique nature of the furniture encouraged 21 22 pedestrian interactions and, in many cases, led to social interactions on and around the benches. We could 23 24 observe that people tend to cluster their public seating and the seating induced curiosity that could 25 26 potentially increase the vitality of a public space. What is great about this process is Benchmark data is 27 28 open to anyone who implements a project, and this is in stark contrast to many of the top down solutions 29 30 31 for measuring public space, such as the LinkNYC. 32 33 34 Based on these findings, we propose an iterative workflow using sensing technologies to evaluate 35 36 the context of the site, test different solutions, and decide on the optimal design solution. The scholarship 37 38 in urban design on evaluating public space often relies on observing the long term effect of urban design 39 40 interventions (Anderson, 1986; Carr, 1992; Çelik et al., 1994; Childs, 2004; Forsyth et al., 2005; Fyfe, 41 42 1998; Jacobs, 1993; Kaplan et al., 1998; Loukaitou Sideris and Ehrenfeucht, 2009; Marcus and Francis, 43 44 1997; Mehta, 2013; Moudon, 1991) and aims to derive universal rules and recommendations that can be 45 46 applied to other sites (Alexander et al., 1977; Whyte, 1980). In light of the rise of low cost and temporary 47 48 urban planning projects for public spaces that are made permanent through the evaluation after their 49 50 51 implementation as seen in the examples in New York and Philadelphia, there is a growing need to 52 53 measure the impact of urban design implementations in a quick, reliable, and scalable way. Following Jan 54 55 Gehl’s approach to conducting on site analysis that provides site specific solutions to create vibrant 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
Page 33 of 39 Environment and Planning B: Urban Analytics and City Science 33 1 2 3 public space, Benchmark adds a digital layer of information to the observation based tools Jan Gehl 4 5 proposed (Gehl and Svarre, 2013). 6 7 8 As with any prototype, further design refinements on both street furniture and sensor box are 9 10 necessary to improve the functionality and performance of the toolkit. For example, relying on GPS 11 12 13 sensors for the locations of benches was highly inaccurate. Alternative approaches for bench orientation 14 15 and location detection by using accelerometer and gyroscope or low energy sensors that transmit 16 17 Bluetooth signals should be considered. Also, we had hoped to measure shade and sun, which is included 18 19 in the Gehl method, but ourFor lower cost Review light sensors performed Only poorly. They mostly told us when 20 21 someone was sitting on the bench rather than whether the bench was in the sun or shade. 22 23 24 As cities across the US and the world increase their levels of engagement with digital 25 26 technologies, Benchmark could serve as an open source way to understand how people use a public space 27 28 and enable public officials to develop public spaces with the end users in mind. This could either be 29 30 through trials organized by the cities themselves or through civic festivals such as (PARK)ing Day or 31 32 33 Better Block. Our positive feedback from Better Block Charlotte suggests this may be an effective way to 34 35 engage the public. However, it is important to note we do not see Benchmark as replacing human 36 37 observers, who can give greater context and describe street scenes with greater nuance. We see this as 38 39 augmenting public space research, freeing human observers to conduct more qualitative analysis without 40 41 being burdened by having to compile quantitative data such as timestamps or specific locations of 42 43 individuals. 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 https://mc04.manuscriptcentral.com/epb
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