Understanding an Urban Park through Big Data

Urban park system on public health: underlying driving mechanism and planning thinking

The driving mechanism and planning thinking of the impact of urban park system on public health the mission of urban geography, urban and rural planning or landscape architecture are to coordinate the relationship between people and places, people and nature. The municipal park system is an important part of the urban green space system. In order to effectively play the role of the urban park system in promoting the health of urban residents. This manuscript studies the coupling relationship between the "urban park system" and the "public health system" by building a coordination model, reveals the driving mechanism of the urban park system affecting the benign development of public health, and clarifies the positive driving effect of urban parks on public health. Finally, based on the analysis results, the manuscript considers the optimal development strategy of urban parks from the macro and micro levels to promote the sustainable development of urban public health.

COVID-19 pandemic changes the recreational use of Moscow parks in space and time: Outcomes from crowd-sourcing and machine learning

The limited access to urban green spaces during the COVID-19 pandemic had a negative impact on the human-nature interaction in cities and human well-being. Number of visitors to green areas, initially declined due to imposed restrictions, was restored after they were lifted as established by several studies across the globe However, little is still known about changes in behavior and preferences of park visitors in the post-COVID time. In this study, we investigated spatial-temporal patterns of recreational activities in the three urban parks in Moscow (Russia) prior, during and after the COVID-19 lockdown (in 2019 and 2020). The selected parks represent two different types: a centrally located park with much infrastructure and open landscapes (Gorky Park) and parks located at the outskirts of the city center with a more forested landscape and little infrastructure (Timiryazevski and Sokolniki parks). Recreational activities were identified based on the analysis of social media photos using machine-learning algorithms. As expected, park closures during lockdown resulted in overall decrease in the number of taken photos. After the parks were re-opened, however, the number of photos did not grow immediately. The number of photos only restored after almost three months, and the visiting peak shifted to autumn. Differences between parks were related to the type of the park and its landscape structure. The lowest decrease in the number of photos was observed for the Timiryazevsky park - a semi-natural green area, while the centrally located Gorky Park was the most affected, likely due to the strictest control measures. In comparison to 2019, photos in 2020 were more evenly distributed across the area in all the three parks. Besides, 'natural' areas became the main attractors for the visitors - photos under 'nature observation' category became the most popular. Spatial distribution of the recreational activities in post-lockdown period was characterized by larger distances between photos, likely corresponding to the social distancing. COVID-19 pandemic highlighted the value of green areas for citizens, but also changed their recreational preferences and overall behavior in parks. The observed shift from high density of visitors around entertainments and attractions in 2019 to a more homogeneous and less dense distribution along the natural zones in 2020 reveals a new pattern in visitors behavior and preference, which shall be considered in spatial planning of the parks. Increasing availability of natural green areas and their integration in urban green infrastructures can become the most relevant policy to consider the crucial role of urban nature as a source of resilience in turbulent times.

Modified Ant Colony Optimization as a Means for Evaluating the Variants of the City Railway Underground Section

The railway is one of the most energy-efficient modes of transport, helping to enhance the environment and public health in cities and agglomerations. In this paper, the authors raise the issue of the construction of an underground railway route in Wrocław (Poland) to allow the organization of the suburban rail system in the agglomeration. There are many concepts for the construction of this route, but so far none has been realized. Therefore, it is important to design the route properly. Here, five options for this tunnel are considered and evaluated. To make such an evaluation, the authors construct a modified ant colony optimization algorithm (ACO). The "classic" algorithm considers the determination of the shortest route. The modification of the algorithm will allow a more accurate analysis of the issue, taking into account more parameters than just the length of the route. These are the location of traffic generators in the city center, the number of inhabitants neighboring the stations, and the number of tram or bus lines integrated with the railway. The presented method and exemplary case study should allow for the evaluation, introduction, or development of the city railway.

Exploring public values through Twitter data associated with urban parks pre- and post- COVID-19

Since school and business closures due to the evolving COVID-19 outbreak, urban parks have been a popular destination, offering spaces for daily fitness activities and an escape from the home environment. There is a need for evidence for parks and recreation departments and agencies to base decisions when adapting policies in response to the rapid change in demand and preferences during the pandemic. The application of social media data analytic techniques permits a qualitative and quantitative big-data approach to gain unobtrusive and prompt insights on how parks are valued. This study investigates how public values associated with NYC parks has shifted between pre- COVID (i.e., from March 2019 to February 2020) and post- COVID (i.e., from March 2020 to February 2021) through a social media microblogging platform -Twitter. A topic modeling technique for short text identified common traits of the changes in Twitter topics regarding impressions and values associated with the parks over two years. While the NYC lockdown resulted in much fewer social activities in parks, some parks continued to be valued for physical activity and nature contact during the pandemic. Concerns about people not keeping physical distance arose in parks where frequent human interactions and crowding seemed to cause a higher probability of the coronavirus transmission. This study demonstrates social media data could be used to capture park values and be specific per park. Results could inform park management during disruptions when use is altered and the needs of the public may be changing.

Using social media data to understand the impact of the COVID-19 pandemic on urban green space use

Having access to and visiting urban green space (UGS) improves liveability and provides considerable benefits to residents. However, traditional methods of investigating UGS visitation, such as questionnaires and social surveys, are usually time- and resource-intensive, and frequently provide less transferable, site-specific outcomes. This study uses social media data (Twitter) to examine spatio-temporal changes in UGS use in London associated with COVID-19 related lockdowns. It compares georeferenced Tweets posted in a 3 month period from 23 March to 23 June for 3 years covering the first lockdown in the UK in 2020, with Tweets for the same period in 2019 and 2021. The results show that (1) the land-use type of Public Park and Garden was the most frequently visited type of UGS, which may be correlated with these UGS areas remaining opening during the lockdown period; (2) the usage of UGS decreased in central London and increased in other areas during lockdown, which may correlated with working from home restrictions; (3) activities were positively associated with Physical activities maybe as a result of allowing people to take a single daily exercise, and (4) people spent more time in UGS areas on weekdays than weekends compared to pre-lockdown. This is the first study to examine social media data over consistent time period before, during and after the lockdown in relation to UGS. The results show that the findings and method can inform policy makers in their management and planning of UGS, especially in a period of social crisis like the COVID-19 pandemic.

A Literature Review of Big Data-Based Urban Park Research in Visitor Dimension

Urban parks provide multiple benefits to human well-being and human health. Big data provide new and powerful ways to study visitors’ feelings, activities in urban parks, and the effect they themselves have on urban parks. However, the term “big data” has been defined variably, and its applications on urban parks have so far been sporadic in research. Therefore, a comprehensive review of big data-based urban park research is much needed. The review aimed to summarize the big data-based urban park research in visitor dimension by a systematic review approach in combination with bibliometric and thematic analyses. The results showed that the number of publications of related articles has been increasing exponentially in recent years. Users’ days data is used most frequently in the big data-based urban park research, and the major analytical methods are of four types: sentiment analysis, statistical analysis, and spatial analysis. The major research topics of big data-based urban park research in visitor dimension include visitors’ behavior, visitors’ perception and visitors’ effect. Big data benefits urban park research by providing low-cost, timely information, a people-oriented perspective, and fine-grained site information. However, its accuracy is insufficient because of coordinate, keyword classification and different kinds of users. To move forward, future research should integrate multiple big data sources, expand the application, such as public health and human–nature interactions, and pay more attention to the big data use for overcoming pandemic. This review can help to understand the current situation of big data-based urban park research, and provide a reference for the studies of this topic in the future.

Patterns of Urban Green Space Use Applying Social Media Data: A Systematic Literature Review

Scientific interest in the potential of urban green spaces, particularly urban parks, to improve health and well-being is increasing. Traditional research methods such as observations and surveys have recently been complemented by the use of social media data to understand park visitation patterns. We aimed to provide a systematic overview of how social media data have been applied to identify patterns of urban park use, as well as the advantages and limitations of using social media data in the context of urban park studies. We used the PRISMA method to conduct a systematic literature analysis. Our main findings show that the 22 eligible papers reviewed mainly used social media data to analyse urban park visitors’ needs and demands, and to identify essential park attributes, popular activities, and the spatial, social, and ecological coherence between visitors and parks. The review allowed us to identify the advantages and limitations of using social media data in such research. These advantages include a large database, real-time data, and cost and time savings in data generation of social media data. The identified limitations of using social media data include potentially biased information, a lack of socio-demographic data, and privacy settings on social media platforms. Given the identified advantages and limitations of using social media data in researching urban park visitation patterns, we conclude that the use of social media data as supplementary data constitutes a significant advantage. However, we should critically evaluate the possible risk of bias when using social media data.

Fine-grained assessment of greenspace satisfaction at regional scale using content analysis of social media and machine learning

Assessing perceptions of green spaces is of considerable interest to developers aiming for sustainable urbanization. However, there are numerous challenges facing the development of a rapid, effective, and fine-grained method to assess large-scale greenspace perception. Survey-based studies of perception yielded detailed assessments of green spaces but lacked regional comparisons. The few big-data-based studies of greenspace perception lacked fine-grained explorations. Therefore, we used content analysis to interpret perception in two ways: perceived frequency and perceived satisfaction, including overall park satisfaction and satisfaction with individual landscape features. We analyzed social media posts about urban parks in Beijing, China. A structured lexicon was developed to capture detailed landscape features, and machine learning was employed to assess satisfaction levels. Both of these techniques performed well in interpreting greenspace satisfaction from volunteered textual comments. A detailed study of 50 parks demonstrated that overall park satisfaction was positive. Additionally, individual landscape features were more influential than frequency of landscape features in affecting satisfaction. Our framework confirmed the potential of online comments as complementary to traditional surveys in assessing greenspace perception, while enhancing our understanding of this perception on a regional scale. Practically, this study can facilitate sustainable policy-making regarding urban green spaces, specifically through offering a structured landscape-feature lexicon, rapid regional comparison of various parks, and an emphasis on quality rather than quantity of landscape features.

Using VGI and Social Media Data to Understand Urban Green Space: A Narrative Literature Review

Volunteered Geographical Information (VGI) and social media can provide information about real-time perceptions, attitudes and behaviours in urban green space (UGS). This paper reviews the use of VGI and social media data in research examining UGS. The current state of the art is described through the analysis of 177 papers to (1) summarise the characteristics and usage of data from different platforms, (2) provide an overview of the research topics using such data sources, and (3) characterise the research approaches based on data pre-processing, data quality assessment and improvement, data analysis and modelling. A number of important limitations and priorities for future research are identified. The limitations include issues of data acquisition and representativeness, data quality, as well as differences across social media platforms in different study areas such as urban and rural areas. The research priorities include a focus on investigating factors related to physical activities in UGS areas, urban park use and accessibility, the use of data from multiple sources and, where appropriate, making more effective use of personal information. In addition, analysis approaches can be extended to examine the network suggested by social media posts that are shared, re-posted or reacted to and by being combined with textual, image and geographical data to extract more representative information for UGS analysis.

Uses and Limitations of Social Media to Inform Visitor Use Management in Parks and Protected Areas: A Systematic Review

Social media are being increasingly used to inform visitor use management in parks and protected areas. We review the state of the scientific literature to understand the ways social media has been, and can be, used to measure visitation, spatial patterns of use, and visitors' experiences in parks and protected areas. Geotagged social media are a good proxy for actual visitation; however, the correlations observed by previous studies between social media and other sources of visitation data vary substantially. Most studies using social media to measure visitation aggregate data across many years, with very few testing the use of social media as a visitation proxy at smaller temporal scales. No studies have tested the use of social media to estimate visitation in near real-time. Studies have used geotags and GPS tracks to understand spatial patterns of where visitors travel within parks, and how that may relate to other variables (e.g., infrastructure), or differ by visitor type. Researchers have also found the text content, photograph content, and geotags from social media posts useful to understand aspects of visitors' experiences, such as behaviors, preferences, and sentiment. The most cited concern with using social media is that this data may not be representative of all park users. Collectively, this body of research demonstrates a broad range of applications for social media. We synthesize our findings by identifying gaps and opportunities for future research and presenting a set of best practices for using social media in parks and protected areas.

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

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.

What Park Visitors Survey Tells Us: Comparing Three Elevated Parks—The High Line, 606, and High Bridge

Many cities have replaced abandoned transportation infrastructure with an elevated park to gain increased economic benefits by developing old fabric. By following this trend, most studies to this point have only focused on the economic rewards from the replacement rather than its uses in the real world. This study aims to understand how park visitors use elevated parks through a park visitors’ survey. The authors selected three representative elevated parks—the High Line in New York City, the 606 in Chicago, and the High Bridge in Farmville—for the study and asked visitors about their activities, perceived benefits, and satisfaction. Results indicate that the 606, a mixed-use elevated park, allows visitors to engage in high-intensity activity, the High Line as an elevated urban park provides visitors public arts and gardens, and the High Bridge as an elevated green park provided visitors with a connection to unique natural scenery. This study, as the first to compare three different elevated parks, contributes to an understanding of who uses elevated parks and how they use elevated parks.