Tweeting the High Line Life: a Social Media Lens on Urban Green Spaces

sustainability

Article Tweeting the High Line Life: A Social Media Lens on Urban Green Spaces

Jisoo Sim 1,*, Patrick Miller 2 and Samarth Swarup 3

1 Korea Research Institute of Human Settlement, Urban Division, Sejong-si 30147, Korea 2 Landscape Architecture Program, College of Architecture and Urban Studies, Virginia Tech, Blacksburg, VA 24061, USA; [email protected] 3 Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904, USA; [email protected] * Correspondence: [email protected]



Received: 18 August 2020; Accepted: 23 October 2020; Published: 27 October 2020


Abstract: The objective of this study is to investigate elevated parks as urban green spaces using social media data analytics. Two popular elevated parks, the High Line Park in New York and the 606 in Chicago, were selected as the study sites. Tweets mentioning the two parks were collected from 2015 to 2019. By using text mining, social media users’ sentiments and conveyed perceptions about the elevated parks were studied. In addition, users’ activities and their satisfaction were analyzed. For the 606, users mainly enjoyed the free events at the park and worried about possible increases in housing prices and taxes because of the 606. They tended to participate in physical activities such as biking and walking. Although the 606 provides scenic observation points, users did not seem to enjoy these. Regarding the High Line, users frequently mentioned New York City, which is an important aspect of the identity of the park. The High Line users also frequently mentioned arts and relaxation. Overall, this study supports the idea that social media analytics can be used to gain an understanding of the public’s use of urban green spaces and their attitudes and concerns.

Keywords: urban green space; elevated park; big data; social media analytics

1. Introduction Social media is emerging as a key data source and is playing an increasingly important role in the urban design and planning process. For example, social media data about urban spaces can help researchers understand urban use, urban area characteristics, and user perceptions [1–3]. In particular, social media data have the potential for enhancing the understanding of human behavior [4,5] and attitudes of a place [6]. Since social media provides a forum for communicating and sharing thoughts [7], it can be used in the design and planning process as a tool for understanding user characteristics. Martí et al. describe how social media and other recent technologies have transformed people’s interactions in urban spaces [8]. As researchers pointed out, the importance of social media analytics in urban design and planning is that social media analytics provide a means to collect data efficiently, gather users’ thoughts directly, and collect past data. Traditional research methods in the design and planning fields fall into two categories. One is a quantitative approach, such as surveys, and the other is qualitative, such as interviews and observational studies [9]. The quantitative approach gathers data directly from the user in the form of surveys and certain types of observational studies. Qualitative studies rely on interviews, community meetings, and self-reporting methods [9]. Field observation has been used in many studies related to urban areas [10–12]. Surveys of anticipated park users are often used to obtain data about the survey participant. With surveys, it easy to verify who the participant is and the extent to which they are

Sustainability 2020, 12, 8895; doi:10.3390/su12218895 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 8895 2 of 18 representative of park users. In many studies, a survey is used to understand people’s attitudes and their behavior in urban open spaces such as a park. Peters et al. used a survey to find that urban parks can promote social cohesion [13]. Whiting et al. used a survey of park visitors to identify motivations and preferences for outdoor recreation [14]. While traditional assessment tools can be used to evaluate on-site benefits of parks through surveys, interviews, and observations [10,15–17], the weakness of these methods is that preconceptions of the researcher can be reflected in the questions being asked or, in some cases, not asked. If a researcher asks a question with a positive framing, a respondent tends to answer positively. Although traditional methods have strengths, these approaches also have limitations compared to the new method as follows. They are often cost and labor intensive. In addition, they are static in time [18]. Social media has some advantages when compared to these traditional approaches. Though social media analytics have become quite common in several fields of research in the last ten years, its use in landscape planning and design is less prevalent. Social media, as a means of accessing users’ thoughts and attitudes toward a place, can be used in landscape architecture to figure out how people react to and interact with a landscape or places. Historically, landscape architecture has attempted to investigate the relationship between humans and landscape. To do this, many studies have developed methods for understanding how people react to the landscape through survey, observation, and other tools. Social media data, since they are user-generated, offer a new mode of observation. Users post their thoughts and behavior on a social media platform to share with other users, not just with the researchers. The objective of this study is to understand elevated parks as urban green spaces through social media. The research questions are (1) What do people use elevated parks for? and (2) What are people’s opinions about elevated parks? The objective of the first question is to understand how the public perceives elevated parks through their social media postings. Although social media analytics have limitations, they also have the potential to scan and understand broad swathes of public opinion. In terms of understanding elevated parks, this approach allows the researcher how the main themes of public concern relate to the elevated parks. The second question is related to public attitudes toward elevated parks and the way social media data can provide useful information on user satisfaction. They do this by examining the results from activity detected on a short media post and user sentiment about the activity. This study identifies a way to interpret social media data and provide useful information for designers and planners. This article is organized into five sections: Introduction, Literature Review, Methods, Results, and Conclusion. The literature review answers three questions: (1) what constitutes social media, (2) what social media analytics are, and (3) how researchers obtain insights from social media. After reviewing previous studies, data collection and data analytics are introduced in the Methods section. This section presents the different analytics in detail, as well as the findings from the analytics. From these findings, the Conclusion draws implications for the future use of social media as a lens to understand urban green spaces.

2. Social Media

2.1. Defining Social Media The emergence of Big Data has opened up new possibilities for urban studies. Among Big Data sources, social media comprises internet-based applications that provide services to users to create and share user-generated content [19,20]. Social media has reshaped our way of thinking about user data and become an important data source in multiple academic fields, such as urban studies, humanities, geography, and sociology. Social media has grown dramatically because of the emergence of the “Web 2.0” features [21], the development of internet accessibility, and increase in smartphone use [22]. Web 2.0 is a collection of technologies which allow users to create content, interact with other users, and share information [20]. These developments are particularly well-suited to landscape Sustainability 2020, 12, 8895 3 of 18 architecture, because these new data sources can offer insights into how local environments are used and experienced. Kaplan and Haenlein classify social media into several categories [20]. The first type is collaborative media, such as Wikipedia. The second one is designed to share content, such as YouTube and Flickr. The third one is social networking platforms, such as Twitter and Facebook. The fourth one is blogging platforms, such as WordPress and Tumblr. Social media improves public engagement tools in two ways: social media contributes to augmenting public engagement, and social media works as public communication channels. Regarding public engagement, many researchers agree that social media use increases public engagement [23], though a well-known limitation is that the population of social media users skews heavily towards younger age groups. On the other hand, this bias contributes to increasing the young generation’s participation in urban planning [24]. Fredericks and Foth describe how social media can be used as a means of augmenting public participation in planning [25]. Social media becomes a virtual public sphere by providing supplementary channels to traditional ones. In terms of a new public sphere, social media also provides a new communication space for the public. According to Jendryke et al., social media helps us to understand many issues in a city, such as disasters, emergencies, and others [26]. Brkovic and Stetovic also describe the importance of social media in terms of communication within communities. The authors explain that social media platforms act as the outreach channels of urban planners to communicate with residents for gathering information, announcing news, and sharing resources [27]. Sui and Goodchild mention the use of social media platforms to support communication among citizens [28].

2.2. Social Media Analytics Text mining, also known as text analytics, allows researchers to reveal the user attitudes behind textual data. This field, which is at the intersection of information retrieval, data mining, statistics, and linguistics, extracts meaningful information from unstructured textual data [29]. The emergence of social media applications has contributed to the growth of text mining usage. Text mining borrows techniques from several areas, such as Natural Language Processing (NLP), Machine Learning (ML), Information Retrieval (IR), and Data Mining (DM). Hu and Liu describe the text mining process in three phases: text pre-processing, text representation, and knowledge discovery [30]. Text pre-processing, also known as data processing, makes the textual data into a consistent form to enable text analysis. This step includes stop word removal and stemming. Stop word removal deletes all stop words which are considered meaningless in textual data. Stemming turns words to their stems based on root form [30]. By inflecting words to their root form, the representation has reduced variability. Other text pre-processing steps are lower-casing, normalization, and noise removal. Lower-casing changes all capitalization to lowercase to prevent the sparsity issue that the same words with difference cases map to the same form [31]. Normalization transforms text into a standard form. For example, it maps multiple forms of one word to a single normalized form, such as “stopwords”, “stop-words”, and “stop words” to “stopwords”. It also changes a misspelled word to a standard form. For example, the words “wooooow” and “wuw” to “wow”. Text pre-processing may differ according to the research design and purpose. In the case of analyzing tweets, most researchers eliminate hashtag, url, html, ID, and etc. before conducting text analytics. The next step in text mining is text representation. This is a way to represent words into sparse vectors by word, known as the “Bag of Words (BOW)” representation. The basic assumption of the model is to slice textual information by word and count each word’s frequency to visualize it [32]. For this mode, a clustering algorithm is widely used to visualize words. In the Bag of Words model, a sentence or paragraph is the bag of its words (Table1). Sustainability 2020, 12, 8895 4 of 18

Table 1. Example of BOW.

Sentences Bag of Words (BOW) (1) I walk on the High Line “I”, “walk”, “on”, “the”, “High”, Line” (2) and you also walk on the High Line “and”, “you”, “also”, “walk”, “on”, “the”, “High”, Line”

The Bag of Words model contributes to detecting and identifying topics and activities in urban green spaces. Lim et al. examine park visitor sentiments and activities using text analytics [33]. In this study, the authors use Latent Dirichlet Allocation (LDA) to detect topics and activities in tweets about urban green spaces. The authors identify two main findings. One is that common activities occurred in larger parks that had food, drinks, and events and another is that park visitors liked to take a photo at prominent landmarks or historical places. These results may seem like a predictable finding that is commonly mentioned in studies [34]. However, the main contribution of this study is to examine the use of text analytics to detect behaviors in parks. A refinement of the simple Bag of Words approach is the N-gram language model. N-gram is a sequence of N words in the textual data [35], which means a set of adjacent sub-sequences of words within a given text. For example, for the sentence “I walk in the High Line”, when N is 2 (bigram), the sentence can be divided into “I walk”, “walk in”, “in the”, “the High”, and “High Line”. When N is 3 (trigrams), the results are “I walk in”, “walk in the”, “in the High”, and “the High Line”. N-grams are widely used in statistical NLP for calculating the joint probability of a sentence [36]. For using vector scores and the probability of a sequence, many studies use the N-gram model to detect issues [37], behavior [38], sentiment [39], and social unrest [40]. Agrawal et al. use the term frequency model to monitor a subset of the stream elements [41]. In the case of detecting behavior, Myslin et al., in 2013, conducted a study to understand and develop a machine learning classifier to detect behaviors and attitudes to tobacco [38]. They collected over 7000 tweets related to tobacco from 2011 to 2012 and manually classified it. Using the data, they developed machine learning classifiers that were trained to detect sentiment [38]. In this study, a bigram model was used to calculate and predict the probability of sequential words for the machine learning classification algorithm. For the last processing phase in text analytics, several methods such as classification, clustering, event detection, and sentiment analysis are used [30]. Among these tools, sentiment analysis is commonly used in text analytics, especially for analyzing social media data. According to Mukherjee, sentiment analysis is a means of understanding a user’s feelings posted in positive or negative tweets by analyzing texts. It can be considered the study of people’s opinions, sentiments, emotions, and attitudes [42]. In this study, we use the lexicon-based approach for sentiment analysis. The lexicon-based approach uses positive and negative sentiment terms by using a dictionary based-approach [43]. The dictionary-based approach collects a small set of opinion words and then grows this set by searching in a large collection of text [44]. There are two ways to detect the sentiment in textual information: polarity analysis and emotion analysis. Polarity analysis classifies sentiment into three categories, negative, positive, and neutral. Emotion analysis categorizes feelings as happy, sad, fearful, angry, surprised, and disgusted [45]. Although sentiment analysis is helpful to understand feelings and emotions expressed by social media users, this kind of analysis can be challenging because of the brevity of the postings and a tendency to use abbreviated words [46]. To reduce its difficulties and increase its validity, Hutto and Gilbert created a model known as Valence Aware Dictionary for sEntiment Reasoning (VADER) [46]. They examined the validity of a sentiment lexicon and then combined lexical features with five rules embodying words. After the authors developed the VADER, they examined its accuracy in social media data by comparing the results from the VADER with ground truth. The authors claim the strengths of VADER as follows: (1) it works well on short and abbreviated text, (2) it is constructed from a human-created sentiment lexicon, and (3) it is fast enough to analyze streaming data [46]. Sustainability 2020, 12, 8895 5 of 18

They developed a new sentiment lexicon list from existing sentiment lists such as Linguistic Inquiry and Word Count [47], Affective Norms for English Words, and General Inquirer and added human-created lists. To make a human-created lexicon list, they used a wisdom-of-the-cloud (WotC) to estimate a point for the sentiment for each feature. Each feature was rated with a score from extremely negative, 4, to extremely positive, 4, and zero for neutral by human raters. By doing so, − they identified 7500 lexical features with validated score indicating the sentiment polarity. With these lexicons, VADER measures each word’s sentiment score and sums the sentiment score over the text. Then, VADER normalize scores to be between 1 and +1. Positive sentiment refers to this score being − larger than 0.05, and negative sentiment is under 0.05; otherwise, it is considered neutral (when the − score is larger than 0.05 and smaller than 0.05). Compared to other sentiment analysis lexicons, − VADER does well at estimating human sentiment, especially in tweets [46].

2.3. Related Studies Based on the attributes of social media data, researchers use it in detecting landscape and land uses, supporting public engagement, and identifying the relationship between people and place. In this part, we introduce studies that used social media data as a major data source in urban studies and landscape architecture. Since studies in landscape architecture are comparably fewer than urban studies, social media use in urban studies is mainly discussed. Many studies aim to (1) detect hidden functions of urban areas and (2) detect hidden relationship between human and landscape. First, social media allows characterization of the use of urban areas, green spaces, and protected areas. Tu et al. discover urban functions by using social media data [48]. From the data, the authors extracted the behaviors of users and identified the time of posting. By doing so, they reveal the use of urban areas by functions such as working and time [48]. In a similar study, Chen et al. depict urban functions by using hot place analysis with social media data [49]. The authors identify the urban functions and places that emerge as central places. Silva et al. show that using social media data allows the detection of characteristics of each urban area [50]. In urban planning, many studies use social media to find functional areas. Studies in landscape architecture also use social media to detect the ecosystem. Oteros-Rozas et al. examine over a thousand photos from Flickr and Panoramio to identify the relationship between landscape diversity and ecosystem services [51]. Landscape diversity is an important aspect to assess the quality of the landscape and cultural ecosystem services refer to the benefits of green spaces to people. The authors find a weak and positive relationship between the two factors [51]. Second, social media provides a chance to understand how people interact with the landscape. Tieskens et al. used photos in Flickr and Panoramio to examine the relationship between landscape attributes and preferences [52]. Many researchers in landscape architecture have similarly focused on the relationship between landscape attributes and people’s preferences [52]. However, most studies use surveys with photographs, which means there is a gap between the photos as representative media and real places. These studies may reduce the gap between the photos and reality by using visitors’ uploaded photos. Contents and multi-media information posted on social media platforms allow researchers to detect a new use of urban place and the relations between human and nature. In particular, social media data contain geo-location information, which contributes to understanding how people use a place. Social media studies which use geotagged information often provide meaningful information for the design and planning. Wu examined the use of urban trails by examining geo-location information from Flickr and Twitter [53]. The author aimed to examine the usefulness of social media as a proxy for demand. By mapping geotagged postings along the trails, they make heat maps. At the same time, they calculate trail traffic to compare two datasets. As a result, the authors find a weak meaningful statistical correlation between photos and trail traffic [53]. Fisher et al. also attempted to predict trail traffic by using social media [54]. They measured the traffic from various techniques including geotagged photos and compared the traffic. They found that correlations between official statistics and Sustainability 2020, 12, 8895 6 of 18 geotagged photos are between 55% and 95%. Based on the results, they assert that user-generated data can let urban planners and landscape architects manage and monitor trail use [54]. 6 of 19

3. Methods Methods

3.13.1.. Study Study Sites Sites Two well-knownwell-known and popular elevated parks, the 606 in Chicago and the High Line Line in in New New York York CityCity,, were selected for this study. Their location in urban areas and the large number of people who visit these parks makes them ideal for a social media-basedmedia-based study.study. The 606 waswas thethe Bloomingdale Bloomingdale line line that that was was an an elevated elevated railroad railroad crossing crossing Chicago Chicago in the in east–westthe east– wdirection.est direction. The railroad The railroad was constructed was constructed in 1873 in and 1873 was and elevated was elevatedin the 1910s in tothe reduce 1910s pedestrian to reduce pedestrianfatalities. The fatalities. line was The used line for was passenger used for trains passenger but stopped trains its but operation stopped in its 2001. operation After this,in 2001. from After 2002 thisto 2004,, from the 2002 greenway to 2004, concept the greenway was introduced concept was to the introduced public. A to group the public. known A as group Friends known of the as FriendsBloomingdale of the TrailBloomingdale launched inTrail 2003 launched for advocacy in 2003 [55 for]. Inadvocacy 2015, the [55]. 606 openedIn 2015, asthe the 606 longest opened elevated as the parklongest in theelevated U.S. (see park Figure in the1 )U.S. (see Figure 1)

(a) (b)

Figure 1. ((aa)) The The 606, 606, ( (b)) The High Line (c (copyopy right Jisoo Sim Sim).).

The High High Line Line was was the the street street-level-level railroad railroad track track along along Tenth Tenth and andEleventh Eleventh on Manhattan on Manhattan’s’s West WestSide in Side 1847. in In 1847. 1929, In when 1929, safety when safetyissues were issues raised, were raised,the city, the the city, state, the and state, New and York New Central York Centralagreed onagreed the West on the Side West Improvement Side Improvement Project Projectled by Robert led by Moses Robert [56] Moses and [56 the] and street the-level street-level tracks turned tracks intoturned the into elevated the elevated railway. railway. The invention The invention of standard of standard container container cargo cargo fostered fostered the the growth growth of of the interstate trucking system during the 1950s [57] [57] and it also led to shrinkage of rail traffic traffic throughout the U.S. The owner Conrail decided to disconnect the High Line elevated railway from the national rail system in 1980 for a year. While the railway was reconnected in 1981, there was no demand to use this line. The tracks ceased operation at this point. The The High High Line Line park opened in 2009 and has achieved economic success success,, be becomingcoming a ann iconic elevated park. In In 2015, 2015, seven seven years years after after its its opening, opening, 7.6 million people visited the park park,, which was was nearly nearly six six t timesimes the the number number of of visitors visitors in in the the first first year, year, 1.3 million [[58]58].. ItIt isis alsoalso expected expected to to generate generate around around USD USD 1 billion1 billion in in tax tax revenues revenues to theto the city city over over the thenext next 20 years 20 years [59]. [59] The. successThe success of the of High the LineHigh has Line led has other led cities other to cities want to to want build to their build own the elevatedir own parkselevated to reviveparks to struggling revive struggling communities. communities.

3.2. Data Data Collection Collection We chose Twitter Twitter for for social social media media analytics analytics in this in this study study due due to several to several reasons: reasons: (1) Twitter (1) Twitter users generateusers generate short shortmessages messages,, limited limited to 280 to characters 280 characters (until (until late 2017, late 2017, the limit the limit was was140 140characters) characters),, (2) (2)Twitter Twitter is convenient is convenient to toaccess access and and collect collect data, data, and and (3) (3) many many studies studies use use Twitter Twitter as as the the main main source. source. We introduce Twitter Twitter in in general, general, its its users, users, and and its its data data structure. structure. Following Following the theapproach approach used used by Liu by [42]Liu, [ 42we], codified we codified keyword keyword-based-based queries queries related related to toeach each elevated elevated park. park. We We submitted submitted each each keyword keyword to Twitter’s search interface to access full historical tweets instead of using the streaming API. By doing so, we collected tweets posted from January 2015 to June 2019 for each elevated park (Table 2).

Sustainability 2020, 12, 8895 7 of 18 to Twitter’s search interface to access full historical tweets instead of using the streaming API. By doing so, we collected tweets posted from January 2015 to June 2019 for each elevated park (Table2).

Table 2. Total number of tweets.

Number of Tweets Keywords Total Number of Tweets for the Study (Before Text Pre-Processing) The 606 “The 606”, “the Bloomingdale trail” 14,340 12,952 The High Line “The High Line” 206,229 165,347

The data only include tweets posted in English. Since most analytics in text mining including sentiment analysis are developed for the English language, using non-English language data in the analysis derives inaccurate results [60]. To reduce the inaccuracy, we only include English tweets. Among the total tweets including keywords, we exclude all tweets posted in other languages except English. For the 606 (n = 14,340), we deleted 497 tweets (English tweets = 13,843). For the High Line, after the keyword search (n = 206,229), all tweets using other languages other than English were deleted. After pre-processing for text mining, we reviewed the data for each park and removed unrelated tweets for reducing inaccuracy. First, we plotted the number of tweets by day for each elevated park for detecting abnormal dates. For the abnormal dates, we reviewed all tweets posted on these dates and identified keywords to delete unrelated tweets. Second, we found heavy users to detect unrelated tweets. After finding heavy users who posted more than the average level, we reviewed the profiles of these users on Twitter. For example, unrelated tweets use the elevated park name as their business in other cities. In the case of the 606, there is a jazz bar, “the 606 Club”, in London. One of the model names of a product uses “High Line”. These are examples of unrelated tweets. If a tweet included unrelated keywords such as the club name or product name, these tweets were eliminated before the analysis. Through these filtering processes, we deleted tweets which were not related to the elevated park. The total number of tweets of the 606 is 12,952 and the High Line is 165,347.

3.3. Data Analytics After the text pre-processing, we conducted three main analyses: (1) topic analysis, (2) sentiment analysis, and (3) text analysis. Topic analysis is conducted to discover the main issues being discussed about each elevated park by month. Sentiment analysis aims to detect the public sentiment towards the elevated parks. For sentiment analysis, we used the VADER model, which was developed especially for social media data [46], because of its accuracy, especially for a short text, compared to other sentiment analysis models. Text analysis, at a more basic level, looks at the term frequency of selected keywords and the relationship between corpora. We selected keywords related to physical activities, social activities, nature, and scenery based on the survey questionnaire, then compared the sentiment of each activity. Through the analysis, the relationship between activity and sentiment in the virtual environment can be revealed.

4. Results

4.1. Public Attitudes about the Elevated Park

4.1.1. The 606 Regarding the 606, the number of tweets and the main topics of tweets relate to the residential location of the park and its proximal neighborhoods. The dates with the top five numbers of tweets show some of the main issues people are interested in. One of the abnormal dates was an event day for residents—on the first anniversary of the 606—and another one was the announcement of a block party in the 606. Two days were related to crime in the 606, such as theft and robbery. One of the dates was a protest of rising rents, property prices, and taxes. These show that the interests of residents Sustainability 2020, 12, 8895 8 of 18

8 of 18 and frequent park visitors tend towards their neighborhoods and their communities. In terms8 of ofthe 18 number of tweets, we find that users mentioned the 606 mostly in May, July, September, and October. and October. The number of tweets by year shows a decrease by year. Since the 606 opened in June andThe October. number ofThe tweets number by year of tweets shows by a decrease year shows by year. a decrease Since the by 606 year. opened Since in the June 606 2015, opened thecollected in June 2015, the collected tweets include the tweets from August 2015 to July 2019 (Figure 2). 2015,tweets the include collected the tweets tweets include from August the tweets 2015 from to July August 2019 (Figure 2015 to2 ).July 2019 (Figure 2).

Figure 2. (a) Number of tweets by month, (b) Number of tweets by year. FigureFigure 2. 2. (a)(a )Number Number of of tweets tweets by by month, month, (b) (b )Number Number of of tweets tweets by by yea year.r. The topic analysis reveals the changes in topic by month (Figure 3). During the winter season, TheThe topic topic analysis analysis reveals reveals the the changes changes in in topic topic by by month month (Figure (Figure 3).3). During During the the winter winter season, season, from December to February, park users frequently mentioned “light” and “walk” (or “walking”). fromfrom December December to to February, February, park park users users frequently frequently mentioned mentioned “ “light”light” and and “ “walk”walk” (or (or “ “walking”).walking”). Compared to the other season, “jazz” was also mentioned in January and February. Some of the main ComparedCompared to to the the other other season, season, “ “jazz”jazz” was was also also mentioned mentioned in in January January and and February. February. Some Some of of the the main main topics of the spring were worries such as “robberies”, “crime”, and “gentrification”. From March to topictopicss of of the spring werewere worries worries such such as as “robberies”, “robberies “crime”,”, “crime and”, and “gentrification”. “gentrification From”. From March March to May, to May, people tweeted worries liked “robberies”, “police”, “crime”, “night”, “gentrification”, and May,people people tweeted tweeted worries worries liked “robberies”, liked “robberies “police”,”, “police “crime”,”, “ “night”,crime”, “gentrification”,“night”, “gentrification and “housing”.”, and “housing”. They also mentioned “tensions” and “buzzing” to express their feelings. Among the “Theyhousing also”.mentioned They also “tensions” mentionedand “tensions “buzzing”” and to “ expressbuzzing their” to feelings. express their Among feelings. the spring Among months, the spring months, April shows a different result compared to other months. In April, people mentioned springApril showsmonths, a di Aprilfferent shows result a compareddifferent result to other compared months. to In other April, months. people In mentioned April, people activities mentioned such as activities such as “bike” and “walk” frequently, and events such as “free jazz night”. In the summer activities“bike” and such “walk” as “bike frequently,” and “walk and events” frequently, such as and “free event jazzs night”.such as In“free the jazz summer night season,”. In the park summer users season, park users mentioned physical activities such as “walk”, “bike”, “run”, with their status such season,mentioned park physicalusers mentioned activities physical such as activities “walk”, “bike”,such as “run”,“walk” with, “bike their”, “run status”, with such their as “happiness”. status such as “happiness”. For the last, during the fall season, people liked to take “photos”. Compared to other asFor “happiness the last, during”. For the last, fall season,during peoplethe fall likedseason, to takepeople “photos”. liked to Comparedtake “photos to” other. Compared seasons, to people other seasons, people mentioned “photos” in the fall season more often. They also tweeted “beautiful” in seasonsmentioned, people “photos” mentioned in the “photos fall season” in the more fall often.season Theymore alsooften. tweeted They also “beautiful” tweeted “ inbeautiful November,” in November, which was the first time that they described their feelings about the scenery. Overall, park Novemberwhich was, which the first was time the thatfirst time they that described they describe their feelingsd their feeling abouts the about scenery. the scenery. Overall, Overall, park userspark users worried about “housing”, “prices”, and “crimes” in the 606. Generally, they liked to do physical usersworried worried about about “housing”, “housing “prices”,”, “prices and”, and “crimes” “crimes in” in the the 606. 606. Generally, Generally, they they likedliked to do physical physical activities during all seasons; however, the intensity of physical activity may differ from season to activitiesactivities during during all all seasons seasons;; however, however, the the intensity intensity of of physical physical activit activityy may may differ differ from from season season to to season, from light walking in the winter to running in summer. season,season, from from light light walking walking in in the the winter winter to to running running in in summer. summer.

Figure 3. Topic changes by month (x: %). Figure 3. Topic changes by month (x: %). Figure 3. Topic changes by month (x: %). For sentiment analysis, users tend to mention the 606 positively. When we compared the For sentiment analysis, users tend to mention the 606 positively. When we compared the sentiment by year, the histogram shows that positive sentiments increased by year. Although the sentiment by year, the histogram shows that positive sentiments increased by year. Although the

Sustainability 2020, 12, 8895 9 of 18 9 of 18 9 of 18 totalFor number sentiment of analysis,tweets decreased users tend by to year, mention users the tend 606 positively.ed to post Whenabout wethecompared 606 more thepositively sentiment than total number of tweets decreased by year, users tended to post about the 606 more positively than bynegatively year, the histogram (Figure 4a). shows An thatinteresting positive aspect sentiments is the increasedsentiment by of year.summer Although and winter the total (Figure number 4b).of The negatively (Figure 4a). An interesting aspect is the sentiment of summer and winter (Figure 4b). The tweetsdifference decreased in sentiment by year, users by tended month to is post slight, about but the the 606 positive more positively sentiment than percentage negatively (Figure of July4a). and difference in sentiment by month is slight, but the positive sentiment percentage of July and AnDecember interesting isaspect higher is than the other sentiment months of summer (Figure 4c). and These winter may (Figure reflect4b). occasions The di ff erencesuch as in Independence sentiment December is higher than other months (Figure 4c). These may reflect occasions such as Independence byDay month and is Christmas. slight, but the positive sentiment percentage of July and December is higher than other Day and Christmas. months (Figure4c). These may reflect occasions such as Independence Day and Christmas.

(a) (b) (c) (a) (b) (c) Figure 4. (a) Sentiment per month, (b) Sentiment by month, (c) Sentiment by year of the 606. Figure 4. (a)(a) Sentiment per month, ((b)b) Sentiment by mo month,nth, (c) (c) Sentiment by year of the 606. To scrutinize the sentiment analysis, we conducted bigram network analysis for each sentiment tweet.To scrutinizescrutinize Users positively thethe sentimentsentiment posting analysis,analysis, about the wewe 606 conductedconduc wereted celebrating bigram network birthdays, analysis free and for cheap each sentiment events, and tweet.a block Users party positively (Figure postingposting 5a). For aboutabout the negative thethe 606606 were weretweets, celebrating celebrating users mentioning birthdays, birthdays, the free free 606 and and focused cheap cheap events,on events, protest and and and a blocka blockgentrification, party party (Figure (Figure affordability5a). 5a). For For the theand negative negative housing, tweets, tweets, robberies users users and mentioning mentioning police, surrounding the the 606 606 focused focuseds and tensions, on on protest protest and andand bike gentrification,gentrification,robbing (Figure aaffordabilityffordability 5b). These and tweets housing, show robberies issues which and police, users surroundingsworried about, and with tensions, two main and themes: bike robbingone is (Figuregentrification,5 5b).b). TheseThese and tweetstweetsthe other showshow one issuesissues is crime whichwh. ich usersusers worriedworried about,about, withwith twotwo mainmain themes:themes: one is gentrification,gentrification, and thethe otherother oneone isis crime.crime.

(a) (b) (a) (b) FigureFigure 5. (5.a) (a) Network Network diagram diagram for for positive positive sentiments, sentiments, (b) (b) Network Network diagram diagram for for negative negative sentiments. sentiments. Figure 5. (a) Network diagram for positive sentiments, (b) Network diagram for negative sentiments. 4.1.2.4.1.2. The The High High Line Line 4.1.2. The High Line TheThe High High Line Line had ha thed the most most tweets tweets (n (n= 165,347)= 165,347) compared compare tod to other other elevated elevated parks parks in in our our dataset. dataset. AfterAfterThe text text High preprocessing, preprocessing, Line had the a total mosta total of tweets 165,347of 165,347 (n tweets= 165,347) tweets were were compared identified identified to for other for the theelevated analysis analysis parks and and this in this our was was dataset. almost almost elevenAftereleven text times timespreprocessing, more more than than the a thetotal 606 606 tweetsof 165,347tweets (n =(n tweets14,340). = 14,340). were Like Like identified the the 606, 606, for we we the reviewed reviewed analysis the andthe abnormal abnormalthis was dates almost dates to to identifyelevenidentify times big big events more event orthans issuesor issuethe in606s the in tweet the High Highs Line.(n = Line. 14,340). However, However, Like there the there was606, was no we special noreviewed special event eventthe on abnormal the on top the 10 top dates dates 10 dates ofto totalidentifyof numbertotal big number events of tweets, orof issuestweets except in, forexcept the the High 5thfor Line. Annualthe 5th Ho Coachwever,Annual and there Coach Friends was and no of Friendsspecial the High event of Line the on summerHigh the top Line party10 summer dates on 25of party Julytotal 2015. numberon July Other 25of, datestweets,2015. wereOther except in dates 2015, for werethe which 5th in was2015Annual the, which topCoach tweetedwas and the Friends yeartop tweeted of theof the study year High periodof theLine study (Figure summer period6). party(Figu onre July 6). 25, 2015. Other dates were in 2015, which was the top tweeted year of the study period (Figure 6).

Sustainability 2020, 12, 8895 10 of 18 10 of 18

(a) (b)

FigureFigure 6. 6. (a)(a) Number Number of of tweets tweets by by month, month, (b) (b )Number Number of of tweets tweets by by year year..

RegardingRegarding the the topic topic changes changes by by month, month, the the High High Line Line shows shows more more stable stable patterns patterns in in topic topic comparedcompared to to the the 606. 606. Figure Figure 7 shows the results from thethe topictopic analysis.analysis. Generally, peoplepeople tweetingtweeting aboutabout the High LineLine mentionedmentioned “art”, “art” “walk”,, “walk” “photo”,, “photo” “people”,, “people and”, and “city” “city frequently.” frequently. The The High High Line Lineusers users mentioned mention “photo”ed “photo in all” seasons, in all seasons which indicates, which thatindicates the High that Line the is High a place Line to memorize is a placeand to memorizepost on social and media,post on not social a daily media, visit not place, a daily for the visit majority place, for of users. the majority The High of users. Line users The mentionedHigh Line users“building” mentioned and “tower” “building frequently” and “tower also.” frequently also.

FigureFigure 7. 7. TopicTopic changes changes by by month month.. For the number of tweets by month and year, the High Line users tended to post their experiences For the number of tweets by month and year, the High Line users tended to post their on social media platforms from summer to fall. Moreover, the number of tweets by year decreased experiences on social media platforms from summer to fall. Moreover, the number of tweets by year from 2015 to 2019. Compared to the 606, the High Line users were more concentrated in summer and decreased from 2015 to 2019. Compared to the 606, the High Line users were more concentrated in fall seasons (Figure8). summer and fall seasons (Figure 8).

Sustainability 2020, 12, 8895 11 of11 18of 18 11 of 18

(a) (b) (c) (a) (b) (c) Figure 8. (a) Sentiment per month, (b) Sentiment by month, (c) Sentiment by year of the High Line. Figure 8. ((a)a) Sentiment per month, ((b)b) Sentiment by month, ((c)c) Sentiment by year of the High Line.Line. Sentiment analysis of the High Line tweets reveals that the proportion of both positive and Sentiment analysis of the High Line tweets reveals that the proportion proportion of both both positive positive and and negative tweets has increased over the years (the proportion of neutral tweets has decreased). When negative tweetstweets hashas increased increased over over the the years years (the (the proportion proportion of neutralof neutral tweets tweets has has decreased). decreased). When When we we look at the sentiment changes by month, there are only small differences between months. In the lookwe look at the at sentimentthe sentiment changes changes by month, by month, there there are only are only small small differences difference betweens between months. months. In the In High the High Line case, like the 606, the total number of tweets is decreasing; however, users tend to post LineHigh case, Line like case, the like 606, the the 606, total the number total number of tweets of is tweets decreasing; is decreasing however,; however users tend, users to post tend about to post the about the High Line more positively than negatively. Highabout Line the High more Line positively more positively than negatively. than negatively. The bigram network of positive sentiment represents related keywords (Figure 9a). For example, The bigram network of positive sentiment represents related keywords (Figure9 9a).a). ForFor example,example, one of the clusters includes “im” (I’m), “pretty”, and “good”. Some clusters show the events in the one of thethe clustersclusters includesincludes “im”“im” (I’m),(I’m), “pretty”,“pretty”, andand “good”.“good”. Some clusters show the events in the High Line, including “friends”, “coach”, “annual”, and “5th”. The main cluster shows a number of High Line,Line, including “ “friends”,friends”, “ “coach”,coach”, “ “annual”,annual”, and and “5th “5th”.”. The The main main cluster cluster shows shows a number a number of keywords related to the city. “Central” and “Park” are also frequently mentioned with the High Line. ofkeywords keywords related related to the to thecity. city. “Central “Central”” and and“Park “Park”” are also are alsofrequently frequently mentioned mentioned with withthe High the HighLine. From these results, one of the biggest reasons of the High Line’s success can be considered a scenic Line.From th Fromese results, these results, one of onethe biggest of the biggest reasons reasons of the High of the Line High’s success Line’s successcan be considered can be considered a scenic urban view and the city itself. Regarding the negative sentiments (Figure 9b), the bigram network aurban scenic view urban and view the andcity itself. the city Regarding itself. Regarding the negative the negativesentiments sentiments (Figure 9b), (Figure the 9bigramb), the net bigramwork analysis shows some keywords like “abandoned” with “railroad” and “chelsea” with “site”. An networkanalysis analysis shows some shows keywords some keywords like “abandoned like “abandoned”” with “ withrailroad “railroad”” and “ andchelsea “chelsea”” with with“site “site”.”. An important caveat here is that lexicon-based sentiment analysis methods cannot always extract the full Animportant important caveat caveat here here is that is that lexicon lexicon-based-based sentiment sentiment analysis analysis methods methods cannot cannot always always extract extract the full the sentiment of a complex sentence. Thus, the sentiment analysis here should be considered a starting fullsentiment sentiment of a of complex a complex sentence. sentence. Thus, Thus, the the sentiment sentiment anal analysisysis here here should should be be considered considered a starting point for a deeper understanding of user opinions. pointpoint forfor aa deeperdeeper understandingunderstanding ofof useruser opinions.opinions.

(a) (b) (a) (b) FigureFigure 9. (9.a )(a) Network Network diagram diagram for for positive positive sentiments, sentiments, (b )(b) Network Network diagram diagram for for negative negative sentiments. sentiments. Figure 9. (a) Network diagram for positive sentiments, (b) Network diagram for negative sentiments. 4.2.4.2. Visitors’ Visitors Activities’ Activities and and Satisfaction Satisfaction 4.2. Visitors’ Activities and Satisfaction ThisThis section section addresses addresses the the relationship relationship between between users’ users activities’ activities and and their their sentiments sentiments through through This section addresses the relationship between users’ activities and their sentiments through filteringfiltering a specifica specific activity activity in in the the tweets tweets and and comparing comparing the the sentiment. sentiment. Main Main activities activities in in the the elevated elevated filtering a specific activity in the tweets and comparing the sentiment. Main activities in the elevated parkpark fall fall into into physical physical activities, activities, viewingviewing art, social social activ activities,ities, having having picnic picnics,s, scenic scenic overlook overlooks,s, and park fall into physical activities, viewing art, social activities, having picnics, scenic overlooks, and andcontact contact with with nature nature (Table (Table 3).3). Related keywords ofof eacheach categorycategory were were used used to to filter filter the the relevant relevant contact with nature (Table 3). Related keywords of each category were used to filter the relevant tweets. Then, sentiment analysis was conducted to compare the sentiment between activity-related tweets. Then, sentiment analysis was conducted to compare the sentiment between activity-related

Sustainability 2020, 12, 8895 12 of 18 tweets. Then, sentiment analysis was conducted to compare the sentiment between activity-related tweets and unrelated tweets. Results are as follows: (1) the 606 users posted positively about physical activities, enjoying art, social activities, and contact with nature, and (2) the High Line users posted positively in terms of having picnics and scenic overlooks. These results suggest that the 606 supports residents to use the trail as their daily activity place and the High Line allows users to have a picnic in the elevated park and have a special experience in the center of the city, overlooking the urban view.

Table 3. Number of tweets related to the activity.

Number of Tweets Activity Keywords The 606 High Line N = 730 N = 10,509 Physical activity “walk”, “jog”, “run”, “bike” 13 of 19 (5.64%) (6.36%) 13 of 19 987 15,021(7.62) (9.08) Art “art”,”gallery”, “museum” 177 2408 Social “friend”, “family”, “mom”,”dad”, “people”, “father”, “(7.62)mother” (9.08)(7.62) (9.08) (1.37) (1.46) “friend”, “family”, “mom”, “dad”, 177 2408177 2408 Social Social “friend”, “family”, “mom”,”dad”, “people”, “father”, “mother” 45 445 Picnic “lunch“people”,”, “snack” “father”,, “sandwich “mother””,”food”, “coffee”, “yum(1.37)” (1.46)(1.37) (1.46) (0.35)45 (0.27)445 Picnic ““lunch”,lunch”, “snack “snack”,”, “sandwich “sandwich”,”,”food “food”,”, “coffee”, “yum45” 4454 275 OverlookingPicnic “overlook”, “view”, “see”, “watch” (0.35) (0.27) “coffee”, “yum” (0.35) (0.27)(0.03)4 (0.17)275 Overlooking “overlook”, “view”, “see”, “watch” 4 (0.03)275 (0.17) Overlooking “overlook”, “view”, “see”, “watch” First, in the case of the 606, users were actively involved in (0.03)physical activities(0.17), enjoying arts, socialFirst, interaction, in the case and ofcontact the 606, with users nature were in activelythe elevated involved park in(Table physical 4). Users activities who , wereenjoying involved arts, socialphysical interaction, activity in and the contact606 were with satisfied nature with in the the elevated activity (positivepark (Table = 51.78%) 4). Users compared who were to involvedthe High First, in the case of the 606, users were actively involved in physical activities, enjoying arts, social physicalLine (positive activity = 30.31%). in the 606 Additionally were satisfied, the with percentage the activity of physical (positive activity = 51.78%)-related compared tweets toto thethe High total interaction, and contact with nature in the elevated park (Table4). Users who were involved physical Linetweets (positive reinforces = 30.31%). that the Additionally 606 users were, the more percentage involved of inphysical physical activity activity-related (24.73%) tweets than to thethe Hightotal activity in the 606 were satisfied with the activity (positive = 51.78%) compared to the High Line tweetsLine users reinforces (6.36%). that For the the 606 activities users were involving more involved viewing in art,physical the result activity show (24s. 73%)that than the 606the usersHigh (positive = 30.31%). Additionally, the percentage of physical activity-related tweets to the total tweets Linementioned users arts (6.36%). (33.43%) For much the activities more than involving the High viewing Line users art, (9.08%) the result and show mores positivelythat the 606 (48.02%) users reinforces that the 606 users were more involved in physical activity (24.73%) than the High Line users mentionedthan the High arts (33.43%) Line users much (39.74%). more than In terms the High of social Line users interaction, (9.08%) usersand more of both positively elevated (48.02%) parks (6.36%). For the activities involving viewing art, the result shows that the 606 users mentioned arts thanpositively the Hightweeted Line about users social (39.74%). interaction In terms, which of involves social interaction, experiences users with of their both friends elevated or family. parks (33.43%) much more than the High Line users (9.08%) and more positively (48.02%) than the High Line positivelyThe 606 users tweeted positively about posted social aboutinteraction the social, which interaction involves (87.00%) experiences slightly with more their than friends the Highor family. Line users (39.74%). In terms of social interaction, users of both elevated parks positively tweeted about The(84.50%). 606 users For contactpositively with posted nature, about the 606the socialusers postedinteraction more (87.00%) positively slightly (50.00%) more than than the the user Highs of Line the social interaction, which involves experiences with their friends or family. The 606 users positively (84.50%).High Line For (40.58%). contact However, with nature, when the we606 consider users posted the differences more positively between (50.00%) nature than-related the usertweetss of and the posted about the social interaction (87.00%) slightly more than the High Line (84.50%). For contact Highother Linetweets (40.58%). in positive However, sentiment, when both we considerparks show the similardifferences differences between of nature around-related 8%. Overall, tweets andthe with nature, the 606 users posted more positively (50.00%) than the users of the High Line (40.58%). other606 users tweets posted in positive positively sentiment, about physical both parks activitie shows, viewing similar differencesart, social interaction, of around and8%. contactOverall, with the However, when we consider the differences between nature-related tweets and other tweets in positive 606nature. users posted positively about physical activities, viewing art, social interaction, and contact with sentiment, both parks show similar differences of around 8%. Overall, the 606 users posted positively nature. about physical activities, viewing art, social interaction, and contact with nature. Table 4. Sentiments for each activity in the 606. Table 4. Sentiments for each activity in the 606. Activity Table 4. Sentiments for each activitySentiments in the 606. Activity Sentiments Activity Sentiments

Total Sentiment TotalTotal Sentiment Sentiment

PhysicalPhysical Physical

Arts Arts

Social Social

13 of 19

(7.62) (9.08) 177 2408 Social “friend”, “family”, “mom”,”dad”, “people”, “father”, “mother” (1.37) (1.46) 45 445 Picnic “lunch”, “snack”, “sandwich”,”food”, “coffee”, “yum” (0.35) (0.27) 4 275 Overlooking “overlook”, “view”, “see”, “watch” (0.03) (0.17)

First, in the case of the 606, users were actively involved in physical activities, enjoying arts, social interaction, and contact with nature in the elevated park (Table 4). Users who were involved physical activity in the 606 were satisfied with the activity (positive = 51.78%) compared to the High Line (positive = 30.31%). Additionally, the percentage of physical activity-related tweets to the total tweets reinforces that the 606 users were more involved in physical activity (24.73%) than the High Line users (6.36%). For the activities involving viewing art, the result shows that the 606 users mentioned arts (33.43%) much more than the High Line users (9.08%) and more positively (48.02%) than the High Line users (39.74%). In terms of social interaction, users of both elevated parks positively tweeted about social interaction, which involves experiences with their friends or family. The 606 users positively posted about the social interaction (87.00%) slightly more than the High Line (84.50%). For contact with nature, the 606 users posted more positively (50.00%) than the users of the High Line (40.58%). However, when we consider the differences between nature-related tweets and other tweets in positive sentiment, both parks show similar differences of around 8%. Overall, the 606 users posted positively about physical activities, viewing art, social interaction, and contact with nature.

Table 4. Sentiments for each activity in the 606.

Activity Sentiments

Total Sentiment

Sustainability 2020, 12, 8895 13 of 18

Physical Table 4. Cont.

Activity Sentiments

ArtsArts

Social Social

14 of 19 14 of 19

PicnicPicnic Picnic

Overlooking Overlooking Overlooking

Second,Second, the the High High Line Line users users posted posted positively positively about about having having picnics picnic ands and overlooking overlooking experience experience Second, the High Line users posted positively about having picnicss and overlooking experience ratherrather than than the the 606 606 (Table (Table5). 5) Users. Users of theof the High High Line Line enjoyed enjoyed picnics picnic slightlys slightly more more positively positively (45.61%) (45.61%) ratherrather thanthan thethe 606606 ((Table 5).. UsersUsers ofof thethe HighHigh LineLine enjoyedenjoyed picnicpicnicss slightlyslightly moremore positively (45.61%) thanthan the the 606 606 users users (44.44%). (44.44%). Considering Considering the di thefferences differences between between picnic-related picnic- tweetsrelated and tweets other and tweets other thanthan the the 606 606 usersusers (44.44%). (44.44%). Considering Considering the the differences differences between between picnic picnic--relatedrelated tweets tweets and and other other intweets positive in sentiment, positive sentiment,the 606 users the mentioning 606 users the mention picnic-relateding the keywords picnic-related were keywords slightly more were (44.44%) slightly tweetstweets inin positive positive sentiment, sentiment, the the 606 606 users users mention mentioninging thethe picnic picnic--relatedrelated keywords keywords were slightlyslightly thanmore unrelated (44.44%) tweets than unrelated (42.40%). tweets However, (42.40%). the result However, of the the High result Line of showsthe High the Line larger shows diff erencesthe larger more (44.44%) than unrelated tweets (42.40%). However, the result of the High Line shows the larger betweendifferences them between (45.61% them vs. 32.96%). (45.61% vs. This 32.96%). result suggestsThis result that suggests picnicking that picnicking is a more common is a more activity common differences between them (45.61% vs. 32.96%). This result suggests thatthat picnicking is a more common atactivity the High at Line the than High at Line the 606. than The at the surprising 606. The result surprising is about result overlooking. is about Regarding overlooking. overlooking, Regarding activity at thethe High High Line Line than than at thethe 606. 606. The The surprising surprising result result is is about about overlooking. overlooking. Regarding Regarding moreoverlooking, High Line more users High positively Line users tweeted positively about the tweeted views onabout the the High view Lines on (36.36%) the High rather Line than (36.36%) the overlooking, more High Line users positively tweeted about the viewss on the High Line (36.36%) 606rather (25.00%). than Thethe 606 606 (25.00%). users were The rarely 606 users satisfied were with rarely the satisfied views (25.00%) with the compared views (25.00%) to other compared tweets ratherrather thanthan thethe 606606 (25.00%).(25.00%). The 606 users were rarelyrarely satisfiedsatisfied with the views (25.00%)(25.00%) compared aboutto other the 606tweets (42.21%) about and the the606 overlooking-related (42.21%) and the overlooking tweets of the-related High tweets Line (36.36%). of the High Negative Line (36.36%). tweets toto other tweets about the 606 (42.21%) and the overlooking--relatedrelated tweetstweets of the High Line (36.36%). wereNegative also 25.00% tweets in were the 606 also about 25.00% overlooking. in the 606 about This suggests overlooking. that the This 606 suggests users were that notthe drawn606 users to thewere Negative tweets were also 25.00% in the 606 about overlooking. This suggests thatthat thethe 606 users were parknot for drawn the views. to the Thepark overall for the results views of. The the Highoverall Line results show of that the theHigh High Line Line show users that liked theto High have Line a not drawn to the park for the views.. TheThe overalloverall resultsresults ofof thethe HighHigh LineLine showshow thatthat thethe HighHigh LineLine picnicusers and liked enjoy to have the view a picnic on theand elevated enjoy the park view more on the than elevated the 606 park visitors. more than the 606 visitors. users liked to have a picnic and enjoy the view on the elevated park more thanthan thethe 606606 visitors..

TableTable 5. Sentiments5. Sentiments for for each each activity activity in in the the High High Line. Line. Table 5. Sentiments for each activity in the High Line. ActivityActivity Sentiments Activity Sentiments

Total Sentiment Total Sentiment Total Sentiment

Physical Physical

Arts Arts

Social Social

14 of 19

Picnic

Overlooking

Second, the High Line users posted positively about having picnics and overlooking experience rather than the 606 (Table 5). Users of the High Line enjoyed picnics slightly more positively (45.61%) than the 606 users (44.44%). Considering the differences between picnic-related tweets and other tweets in positive sentiment, the 606 users mentioning the picnic-related keywords were slightly more (44.44%) than unrelated tweets (42.40%). However, the result of the High Line shows the larger differences between them (45.61% vs. 32.96%). This result suggests that picnicking is a more common activity at the High Line than at the 606. The surprising result is about overlooking. Regarding overlooking, more High Line users positively tweeted about the views on the High Line (36.36%) rather than the 606 (25.00%). The 606 users were rarely satisfied with the views (25.00%) compared to other tweets about the 606 (42.21%) and the overlooking-related tweets of the High Line (36.36%). Negative tweets were also 25.00% in the 606 about overlooking. This suggests that the 606 users were not drawn to the park for the views. The overall results of the High Line show that the High Line users liked to have a picnic and enjoy the view on the elevated park more than the 606 visitors.

Table 5. Sentiments for each activity in the High Line.

Activity Sentiments Sustainability 2020, 12, 8895 14 of 18

Total Sentiment Table 5. Cont.

Activity Sentiments

PhysicalPhysical

Arts Arts

Social Social 1515 ofof 1919

Picnic Picnic

OverlookingOverlooking

5.5. Discussion Discussion InInIn this thisthis section, section,section, we we discuss discuss results results in in detail detail based based on on our our research researchresearch questions. questions.questions. 5.1. What do People Use Elevated Parks for? 5.1. What Do People Use Elevated Parks forfor? The 606 is well-integrated into the daily lives of nearby residents. The users liked to bike and walk The 606 is well--integratedintegrated inintoto thethe daily lives of nearby residents.residents. TheThe usersusers likedliked toto bikebike andand along the park, were interested in events, and worried about housing prices and crimes. The clustering walk along the park, were interestedinterested inin events,events, andand worriedworried aboutabout housinghousing pricesprices andand crimes.crimes. TheThe analysis highlights the main activities in the park and issues related to the park. For the main activity, clustering analysis highlights thethe mainmain activitiesactivities inin thethe parkpark andand issuesissues relatedrelated toto thethe park.park. ForFor thethe biking and walking were the most frequently mentioned in the social media platform. This result main activity, biking and walking were thethe mostmost frequentlyfrequently mentionedmentioned inin thethe socialsocial mediamedia platform.platform. supports the idea that the 606, which is designed for biking and walking [61], is well-designed and This result supports thethe ideaidea thatthat thethe 606,606, whichwhich isis designeddesigned forfor bikingbiking andand walkingwalking [61][61],, isis wellwell-- provides for the needs of residents. Interestingly, the cluster analysis points out issues related to designed and provides for the needs of residents. Interestingly, the cluster analysis points out issues gentrification and crime. The sentiment analysis also supports the idea that the 606 users worried relatedrelated toto gentrificationgentrification andand crime.crime. TheThe sentimentsentiment analysisanalysis alsoalso supportssupports thethe ideaidea thatthat thethe 606606 usersusers about these issues. Another interesting finding from the sentiment analysis is that the 606 users rarely worried about thesese issues.issues. AnotherAnother interestinginteresting findingfinding fromfrom thethe sentimentsentiment anaanalysislysis isis thatthat thethe 606606 mentioned the park during the middle of summer and winter. Since the winter in Chicago is too cold users rarely mentioned the park during the middle of summer and winter. Since the winter in to participate in outdoor activity, the low volume of tweets over the winter is not surprising. However, Chicago is too cold to participate in outdoor activity, the low volume of tweets over the winter is not it is interesting that the volume of tweets is also low in the middle of the summer. These results broadly surprising.surprising. However, However, it it is is interesting interesting thatthat the the volume volume of of tweets tweets is is also also low low in in the the middle middle of of the the support the assertion that the 606, which is designed for residential and mixed use, is closely related to summer.summer. TheseThese resultsresults broadlybroadly supportsupport thethe assertionassertion thatthat thethe 606,606, whichwhich isis designeddesigned forfor residentialresidential the daily life of residents. and mixed use, is closely related to the daily life of residents. The High Line users are more drawn to special events at the park. To them, the purpose of visiting The High Line users are more drawn toto specialspecial eventsevents at thethe park.park. To them, thethe purposepurpose ofof the park is to play or to participate in events like “summer”, “party”, “coach”, and “friend”. A large visiting the park is to play or to participate in events like “summersummer”,, “party”,, “coach”,, andand “friendfriend”.. A large proportion may be tourists, since they also mentioned “New York City” frequently.frequently. TThe topic changes by month also support the same argument that the High Line users are happy to visit the park which is located in NYC. The keyword “NY” was mentioned during almost all months. Compared to the 606 users, the High Line users tended to take photos throughout thethe whole year according to the topic changes,, which also could be due to tourism and/or special events..

5.2. What Arere People’’ss OpinionsOpinions about Elevated Parks? Surprisingly, appreciating art in the park has a lower satisfaction level at thethe HighHigh LineLine thanthan atat thethe 606. This is despite the fact that there are many galleries and museums around the High Line, and thethe HighHigh LineLine regularlyregularly exhibitsexhibits artworks.artworks. ThisThis resultresult isis difficultdifficult toto directlydirectly comparecompare andand isis oneone ofof thethe analysses that can only be achieved through social media analysis. In the case of the 606, compared to thethe Highigh Line,ine, therethere areare manymany casescases ofof appreciatingappreciating artart onlyonly duringduring certaincertain seasons.seasons. This may be due toto 606606 visitorsvisitors perceivingperceiving artworkartwork atat thethe parkpark asas anan unexpectedunexpected featurefeature andand thuthuss moremore novelnovel andand pleasing.. Social activities are viewed positively in both parks. This result is presumed to be due to the characteristics of the place as a park and a positive reaction to the activities of being with friends and family.family. Since Since this this study study w was targeted at elevated parks, we did not compare whether these characteristics occurredred inin particularparticular inin thesethese parks.parks. ComparisonsComparisons withwith otherother typestypes ofof parksparks needneed toto be conducted toto seesee ifif thisthis reactionreaction isis aa characteristiccharacteristic ofof elevatedelevated parks.parks.

Sustainability 2020, 12, 8895 15 of 18 proportion may be tourists, since they also mentioned “New York City” frequently. The topic changes by month also support the same argument that the High Line users are happy to visit the park which is located in NYC. The keyword “NY” was mentioned during almost all months. Compared to the 606 users, the High Line users tended to take photos throughout the whole year according to the topic changes, which also could be due to tourism and/or special events.

5.2. What are People’s Opinions about Elevated Parks? Surprisingly, appreciating art in the park has a lower satisfaction level at the High Line than at the 606. This is despite the fact that there are many galleries and museums around the High Line, and the High Line regularly exhibits artworks. This result is difficult to directly compare and is one of the analyses that can only be achieved through social media analysis. In the case of the 606, compared to the High Line, there are many cases of appreciating art only during certain seasons. This may be due to 606 visitors perceiving artwork at the park as an unexpected feature and thus more novel and pleasing. Social activities are viewed positively in both parks. This result is presumed to be due to the characteristics of the place as a park and a positive reaction to the activities of being with friends and family. Since this study was targeted at elevated parks, we did not compare whether these characteristics occurred in particular in these parks. Comparisons with other types of parks need to be conducted to see if this reaction is a characteristic of elevated parks. In general, elevated parks consider scenic overlooks as an important design feature, and in many cases, viewing points are selected and benches from which to view the landscape are placed. Even in the case of the High Line Park, viewpoints were created and benches were placed at many points. The same goes for the 606. Many elevated parks are designed with the intention of wanting visitors to enjoy the pleasure of extended views. Surprisingly, however, the tweets relating to scenic overlooks have the lowest proportion of positive sentiment. This is contrary to the conventional design concept that has been considered so far, and it will be important to consider this result when designing elevated parks in the future.

6. Conclusions In conclusion, we recommend to urban planners and landscape architects who want to make an elevated park that every elevated park has to be used in its own context. For example, the High Line, which is designed for art exhibitions and special events, is used by tourists who want to enjoy New York City. The 606, which is designed for the neighborhoods, sometimes reflects the neighborhoods’ gentrification instead of economic vitality. This study offers an opportunity to show how every elevated park can be used in a different way. Based on this study, urban planners and landscape architects may consider the side effects of building elevated parks in their city. This study addresses one of the reasons for the High Line’s success through social media analytics. As a more well-known place, not only in New York City but also in the world, the volume of tweets indicates that users mentioned the High Line during all seasons except in November and December. Moreover, the number of tweets was over ten times more than the 606 tweets. The results of cluster analysis show that users frequently mentioned New York City and arts with respect to the High Line. Furthermore, the result of sentiment analysis and bigram network support the notion that the city and urban scenery in the park are the most frequently mentioned topics. These results suggest that tourism contributes to the success of the park. One of the other reasons for its success is arts. Users frequently mentioned arts along the elevated park with positive sentiment, which suggests that arts in the park contributes to the success of the High Line. This study shows how social media data can be used to study urban parks. By using social media analytics, we gained some insight into how people think about and use elevated parks. One of the limitations of this study is that it only considered Twitter among social media. Additionally, this study does not consider the population bias that social media users tend to be more young, white, and male [5]. In the future, other social media platforms can be added to enrich the results. Sustainability 2020, 12, 8895 16 of 18

Author Contributions: Study design, writing—original draft, J.S.; study design, writing—review and editing, P.M. and S.S. All authors have read and agreed to the published version of the manuscript. Funding: S.S. was supported in part by the National Science Foundation (NSF) under grant no. SMA-1520359. Conflicts of Interest: The authors declare no conflict of interest.

References

1. Frias-Martinez, V.; Frias-Martinez, E. Spectral clustering for sensing urban land use using Twitter activity. Eng. Appl. Artif. Intell. 2014, 35, 237–245. [CrossRef] 2. Kelley, M.J. The emergent urban imaginaries of geosocial media. GeoJournal 2013, 78, 181–203. [CrossRef] 3. Lee, R.; Wakamiya, S.; Sumiya, K. Urban area characterization based on crowd behavioral lifelogs over Twitter. Pers. Ubiquitous Comput. 2013, 17, 605–620. [CrossRef] 4. Afzalan, N.; Muller, B. The Role of Social Media in Green Infrastructure Planning: A Case Study of Neighborhood Participation in Park Siting. J. Urban. Technol. 2014, 21, 67–83. [CrossRef] 5. Georgiou, T. Understanding the Real World through the Analysis of User Behavior and Topics in Online Social Media. Ph.D. Thesis, University of California, Santa Barbara, CA, USA, 2017. 6. Hollander, J.B.; Shen, Y. Using Social Media Data to Infer Urban Attitudes about Bicycling: An Exploratory Case Study of Washington DC. In City Networks; Springer: Cham, Switzerland, 2017; pp. 79–97. 7. Kankanhalli, A.; Tan, B.C.; Wei, K.-K. Contributing knowledge to electronic knowledge repositories: An empirical investigation. Mis Q. 2005, 29, 113–143. [CrossRef] 8. Martí, P.; Serrano-Estrada, L.; Nolasco-Cirugeda, A. Using locative social media and urban cartographies to identify and locate successful urban plazas. Cities 2017, 64, 66–78. [CrossRef] 9. Roberts, H.V. Using Twitter data in urban green space research. Appl. Geogr. 2017, 81, 13e20. [CrossRef] 10. Birch, E.L. The Observation Man, A Study of William H. Whyte; Planners Press: Chicago, IL, USA, 1986. 11. Gehl, J.; Svarre, B. How to Study Public Life; Island Press: Washington, DC, USA, 2013. 12. Jacobs, J. The Death and Life of Great American Cities; Vintage Books: New York, NY, USA, 1961. 13. Peters, K.; Elands, B.; Buijs, A. Social interactions in urban parks: Stimulating social cohesion? Urban For. Urban Green. 2010, 9, 93–100. [CrossRef] 14. Whiting, J.W.; Larson, L.R.; Green, G.T.; Kralowec, C. Outdoor recreation motivation and site preferences across diverse racial/ethnic groups: A case study of Georgia state parks. J. Outdoor Recreat. Tour. 2017, 18, 10–21. [CrossRef] 15. Carmona, M. Public Places, Urban. Spaces: The Dimensions of Urban. Design; Abingdon-on-Thames: Routledge, UK, 2010. 16. Ewing, R. Using a visual preference survey in transit design. Public Work. Manag. Policy 2001, 5, 270–280. [CrossRef] 17. More, T.A.; Stevens, T.; Allen, P.G. Valuation of urban parks. Landsc. Urban. Plan. 1988, 15, 139–152. [CrossRef] 18. Sim, J.; Miller, P. Understanding an Urban Park through Big Data. Int. J. Environ. Res. Public Health 2019, 16, 3816. [CrossRef][PubMed] 19. Boyd, D.M.; Ellison, N.B. Social Network Sites: Definition, History, and Scholarship. J. Comput. Mediat. Commun. 2007, 13, 210–230. [CrossRef] 20. Kaplan, A.M.; Haenlein, M. Users of the world, unite! The challenges and opportunities of Social Media. Bus. Horiz. 2010, 53, 59–68. [CrossRef] 21. Lin, Y.; Geertman, S. Can Social Media Play a Role in Urban Planning? A Literature Review. In Computational Urban Planning and Management for Smart Cities; Geertman, S., Zhan, Q., Allan, A., Pettit, C., Eds.; Springer International Publishing: Cham, Switzerland, 2019; pp. 69–84. 22. Kitchin, R. The real-time city? Big data and smart urbanism. GeoJournal 2014, 79, 1–14. [CrossRef] 23. Kleinhans, R.; Ham, M.V.;Evans-Cowley,J. Using Social Media and Mobile Technologies to Foster Engagement and Self-Organization in Participatory Urban Planning and Neighbourhood Governance. Plan. Pract. Res. 2015, 30, 237–247. [CrossRef] 24. Schroeter, R.; Houghton, K. Neo-planning: Location-based social media to engage Australia’s new digital locals. Aust. Plan. 2011, 48, 191–202. Sustainability 2020, 12, 8895 17 of 18

25. Fredericks, J.; Foth, M. Augmenting public participation: Enhancing planning outcomes through the use of social media and web 2.0. Aust. Plan. 2013, 50, 244–256. [CrossRef] 26. Jendryke, M.; Balz, T.; Liao, M. Big location-based social media messages from China’s Sina Weibo network: Collection, storage, visualization, and potential ways of analysis. Trans. Gis 2017, 21, 825–834. [CrossRef] 27. Brkovic, M.; Stetovic, B. Harnessing social media for urban planning: An overview. In Proceedings of the 49th international society of city and regional planners congress, Brisbane, Australia, 1–4 October 2013. 28. Sui, D.; Goodchild, M. The convergence of GIS and social media: Challenges for GIScience. Int. J. Geogr. Inf. Sci. 2011, 25, 1737–1748. [CrossRef] 29. He, W.; Zha, S.; Li, L. Social media competitive analysis and text mining: A case study in the pizza industry. Int. J. Inf. Manag. 2013, 33, 464–472. [CrossRef] 30. Hu, X.; Liu, H. Text Analytics in Social Media. In Mining Text Data; Aggarwal, C.C., Zhai, C., Eds.; Springer US: Boston, MA, USA, 2012; pp. 385–414. 31. Ganesan, K. Getting Started with Text Preprocessing for Machine Learning & NLP. Kavita Ganesan, 23 February 2019. Available online: https://kavita-ganesan.com/text-preprocessing-tutorial/ (accessed on 5 August 2019). 32. Zhang, Y.; Jin, R.; Zhou, Z.-H. Understanding bag-of-words model: A statistical framework. Int. J. Mach. Learn. Cyber. 2010, 1, 43–52. [CrossRef] 33. Lim, K.H.; Lee, K.E.; Kendal, D.; Rashidi, L.; Naghizade, E.; Feng, Y.; Wang, J. Chapter 6—Understanding sentiments and activities in green spaces using a social data–driven approach. In Smart Cities: Issues and Challenges; Visvizi, A., Lytras, M.D., Eds.; Elsevier: Amsterdam, The Netherlands, 2019; pp. 77–107. 34. Sather-Wagstaff, J. Picturing experience: A tourist-centered perspective on commemorative historical sites. Tour. Stud. 2008, 8, 77–103. [CrossRef] 35. Broder, A.Z.; Glassman, S.C.; Manasse, M.S.; Zweig, G. Syntactic clustering of the Web. Comput. Netw. Isdn Syst. 1997, 29, 1157–1166. [CrossRef] 36. Martin, J.H.; Jurafsky, D. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition; Pearson/Prentice Hall Upper Saddle River: Upper Saddle River, NJ, USA, 2009. 37. Laniado, D.; Mika, P. Making Sense of Twitter. In The Semantic Web—ISWC 2010; ISWC: Shanghai, China, 2010; pp. 470–485. 38. Myslín, M.; Zhu, S.-H.; Chapman, W.; Conway, M. Using Twitter to Examine Smoking Behavior and Perceptions of Emerging Tobacco Products. J. Med. Internet Res. 2013, 15, e174. [CrossRef] 39. Tang, D.; Wei, F.; Yang, N.; Zhou, M.; Liu, T.; Qin, B. Learning sentiment-specific word embedding for twitter sentiment classification. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Florence, Italy, 22 June 2014; pp. 1555–1565. 40. Compton, R.; Lee, C.; Lu, T.; Silva, L.D.; Macy, M. Detecting future social unrest in unprocessed Twitter data: ‘Emerging phenomena and big data. In Proceedings of the 2013 IEEE International Conference on Intelligence and Security Informatics, Seattle, WA, USA, 4–7 June 2013; pp. 56–60. [CrossRef] 41. Agrawal, D.; Budak, C.; el Abbadi, A.; Georgiou, T.; Yan, X. Big Data in Online Social Networks: User Interaction Analysis to Model User Behavior in Social Networks. In Databases in Networked Information Systems; Springer: Aizu-Wakamatsu, Japan, 2014; pp. 1–16. 42. Liu, B. Sentiment Analysis, Opinion Mining, Emotion Classification; Cambridge University Press: Cambridge, UK, 2015. 43. Maynard, D.; Funk, A. Automatic Detection of Political Opinions in Tweets. In The Semantic Web: ESWC 2011 Workshops; Springer: Berlin/Heidelberg, Germany, 2012; pp. 88–99. 44. Parrott, W.G. Emotions in Social Psychology: Key Readings; Psychology Press: East Sussex, UK, 2001. 45. Zhai, C.; Massung, S. Text. Data Management and Analysis: A Practical Introduction to Information Retrieval and Text. Mining; Morgan & Claypool: Williston, ND, USA, 2016. 46. Hutto, C.J.; Gilbert, E. VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. In Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media, May 2014. Available online: https://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8109 (accessed on 6 August 2019). 47. Pennebaker, J.W.; Francis, M.E.; Booth, R.J. Linguistic inquiry and word count: LIWC 2001. Mahway Lawrence Erlbaum Assoc. 2001, 2001, 71. Sustainability 2020, 12, 8895 18 of 18

48. Tu, W.; Cao, J.; Yue, Y.; Shaw, S.L.; Zhou, M.; Wang, Z.; Li, Q. Coupling mobile phone and social media data: A new approach to understanding urban functions and diurnal patterns. Int. J. Geogr. Inf. Sci. 2017, 31, 2331–2358. [CrossRef] 49. Chen, J.; Ma, B.; Cao, H.; Chen, J.; Fan, Y.; Xia, T.; Li, R. Attention region detection based on closure prior in layered bit Planes. Neurocomputing 2017, 251, 16–25. [CrossRef] 50. Silva, M.D.F.M.; Calijuri, M.L.; de Sales, F.J.F.; de Souza, M.H.B.; Lopes, L.S. Integration of technologies and alternative sources of water and energy to promote the sustainability of urban landscapes. Resour. Conserv. Recycl. 2014, 91, 71–81. [CrossRef] 51. Oteros-Rozas, E.; Martín-López, B.; Fagerholm, N.; Bieling, C.; Plieninger, T. Using social media photos to explore the relation between cultural ecosystem services and landscape features across five European sites. Ecol. Indic. 2018, 94, 74–86. [CrossRef] 52. Tieskens, K.F.; van Zanten, B.T.; Schulp, C.J.E.; Verburg, P.H. Aesthetic appreciation of the cultural landscape through social media: An analysis of revealed preference in the Dutch river landscape. Landsc. Urban. Plan. 2018, 177, 128–137. [CrossRef] 53. Wu, X.; Wood, S.A.; Fisher, D.; Lindsey, G. Photos, tweets, and trails: Are social media proxies for urban trail use? J. Transp. Land Use 2017, 10. [CrossRef] 54. Fisher, D.M.; Wood, S.A.; White, E.M.; Blahna, D.J.; Lange, S.; Weinberg, A.; Lia, E. Recreational use in dispersed public lands measured using social media data and on-site counts. J. Environ. Manag. 2018, 222, 465–474. [CrossRef] 55. Gobster, P.H.; Sachdeva, S.; Lindsey, G. Up on the 606: Understanding the Use of a New Elevated Pedestrian and Bicycle Trail in Chicago, Illinois. Transp. Res. Rec. 2017, 2644, 83–91. [CrossRef] 56. Bighorse, A. The Highline: Past and Present. 13 May 2010. Available online: https://web.archive.org/web/ 20141023213640/http://www.gsd.harvard.edu/pbcote/courses/archive/2010/gsd6447/bighorse/ (accessed on 14 March 2019). 57. Waldheim, C. Landscape as Urbanism: A General Theory; Princeton University Press: Princeton, NJ, USA, 2016. 58. Ganser, A. High Line Magazine: B1G DA+A and Parks | the High Line Blog. The High. Line, 18 January 2017. Available online: http://www.thehighline.org/blog/2017/01/10/high-line-magazine-b1g-da-a-and-parks (accessed on 8 April 2018). 59. Bliss, L. The High Line’s Biggest Issue—And How Its Creators are Learning from Their Mistakes. CityLab 2017. Available online: http://www.citylab.com/cityfixer/2017/02/the-high-lines-next-balancing-act-fair-and- affordable-development/515391/ (accessed on 8 April 2018). 60. Wang, Z.; Joo, V.; Tong, C.; Chan, D. Issues of Social Data Analytics with a New Method for Sentiment Analysis of Social Media Data. In Proceedings of the 2014 IEEE 6th International Conference on Cloud Computing Technology and Science, Singapore, 15–18 December 2014; pp. 899–904. [CrossRef] 61. Sinha, A. Slow landscapes of elevated linear parks: Bloomingdale Trail in Chicago. Stud. Hist. Gard. Des. Landsc. 2014, 34, 113–122. [CrossRef]

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