Assessing Accessibility of Urban Green Spaces Based on Isochrone Maps and Street Resolution Population Data Through the Example of Zalaegerszeg, Hungary

Kolcsár, Ronald András; Informatics, Egyetem Street

doi:10.26471/cjees/2018/013/003

Cited by 25 publications

(13 citation statements)

References 7 publications

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“…However, in the literature, there are contradictory observations regarding the distance to a park from home and its relevance in people's decisions regarding which park to go to. There are studies that completely neglect spatial aspects (mostly when using Twitter data and performing sentiment analysis) [7,[28][29][30], or, on the contrary, studies that only consider spatial aspects of park visits but not the functionality or other attracting factors [31][32][33].…”

Section: The Importance Of Urban Green Areas and Ways To Analyze Theimentioning

confidence: 99%

Beyond Spatial Proximity—Classifying Parks and Their Visitors in London Based on Spatiotemporal and Sentiment Analysis of Twitter Data

Kovács-Györi

Ristea

Kolcsár

et al. 2018

IJGI

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Parks are essential public places and play a central role in urban livability. However, traditional methods of investigating their attractiveness, such as questionnaires and in situ observations, are usually time- and resource-consuming, while providing less transferable and only site-specific results. This paper presents an improved methodology of using social media (Twitter) data to extract spatial and temporal patterns of park visits for urban planning purposes, along with the sentiment of the tweets, focusing on frequent Twitter users. We analyzed the spatiotemporal park visiting behavior of more than 4000 users for almost 1700 parks, examining 78,000 tweets in London, UK. The novelty of the research is in the combination of spatial and temporal aspects of Twitter data analysis, applying sentiment and emotion extraction for park visits throughout the whole city. This transferable methodology thereby overcomes many of the limitations of traditional research methods. This study concluded that people tweeted mostly in parks 3–4 km away from their center of activity and they were more positive than elsewhere while doing so. In our analysis, we identified four types of parks based on their visitors’ spatial behavioral characteristics, the sentiment of the tweets, and the temporal distribution of the users, serving as input for further urban planning-related investigations.

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Section: The Importance Of Urban Green Areas and Ways To Analyze Theimentioning

confidence: 99%

Beyond Spatial Proximity—Classifying Parks and Their Visitors in London Based on Spatiotemporal and Sentiment Analysis of Twitter Data

Kovács-Györi

Ristea

Kolcsár

et al. 2018

IJGI

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“…Easy access to urban green parks is of growing importance in the design of sustainable, safe and viable cities [13], [14]. The researchers neglect some spatial aspects (mostly when performing sentiment analysis using Twitter data) [13], [15], or, on the contrary, studies that consider only the spatial aspects of green park visits [15], [16] and the improvement of health [17]. Even just a limited number of studies have concentrated on the problem of the direct influence of park visits on physical activities or health [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Patterns of Visitors in Urban Green Parks by Mining Social Media Big Data Based Upon WHO Reports

et al. 2020

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Green parks in urban areas are believed to enhance the well-being of residents. The importance of green spaces to support health and fitness in urban areas has recently regained interest. Reports released in 2010-2016 by the World Health Organization (WHO) on urban planning, environment, and health stated that green spaces can have a positive impact on physical activity, social and mental well-being, enhance air quality and decrease noise exposure. We analyzed the number of check-ins in various parks of Shanghai by utilizing geotagged social media network check-in data. This article presents a descriptive study using social media data by obtaining the three-year comparison of spatial and temporal patterns of park visits to raise public awareness that green parks provide a healthy environment that can be beneficial for the wellbeing of urban citizens. We investigated the visitor spatiotemporal behavior in more than 115 green parks in 10 districts of Shanghai with approximately 250,000 check-ins. We examined 3 years of geotagged data and our main findings are: (i) the spatial and temporal variations of users in urban green parks (ii) the gender differences in space and time with relation to urban green parks. The main objective of this article is to present evident data for policymakers on the advantages of providing green spaces access to urban citizens and to facilitate cities with systematic approaches to provide green space access to improve the health of urban citizens. INDEX TERMS Urban green parks, big data, social networks, spatiotemporal, KDE, data mining.

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“…The walking acceptability rate will double in pedestrian-friendly, attractive environments [53], including parks, natural leisure sectors, and traffic-calmed residential streets. Geographical information systems (GIS) are widely used by researchers to analyze the accessibility, with the two basic options being linear buffering and network analysis [54,55].…”

Section: Introductionmentioning

confidence: 99%

Land-Use Dynamics in Transport-Impacted Urban Fabric: A Case Study of Martin–Vrútky, Slovakia

Nozdrovická

Dostál

Petrovič

et al. 2020

Land

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The paper evaluates landscape development, land-use changes, and transport infrastructure variations in the city of Martin and the town of Vrútky, Slovakia, over the past 70 years. It focuses on analyses of the landscape structures characterizing the study area in several time periods (1949, 1970, 1993, 2003); the past conditions are then compared with the relevant current structure (2018). Special attention is paid to the evolution of the landscape elements forming the transport infrastructure. The development and progressive changes in traffic intensities are presented in view of the resulting impact on the formation of the landscape structure. The research data confirm the importance of transport as a force determining landscape changes, and they indicate that while railroad accessibility embodied a crucial factor up to the 1970s, the more recent decades were characterized by a gradual shift to road transport.

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Assessing Accessibility of Urban Green Spaces Based on Isochrone Maps and Street Resolution Population Data Through the Example of Zalaegerszeg, Hungary

Cited by 25 publications

References 7 publications

Beyond Spatial Proximity—Classifying Parks and Their Visitors in London Based on Spatiotemporal and Sentiment Analysis of Twitter Data

Beyond Spatial Proximity—Classifying Parks and Their Visitors in London Based on Spatiotemporal and Sentiment Analysis of Twitter Data

Spatiotemporal Patterns of Visitors in Urban Green Parks by Mining Social Media Big Data Based Upon WHO Reports

Land-Use Dynamics in Transport-Impacted Urban Fabric: A Case Study of Martin–Vrútky, Slovakia

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