Using Twitter data in urban green space research: A case study and critical evaluation

Roberts, Helen

doi:10.1016/j.apgeog.2017.02.008

Cited by 83 publications

(69 citation statements)

References 42 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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“…Natural ecosystems play a variety of roles in urban areas (Keniger et al, 2013;Hunter, Luck, 2015;Ives et al, 2017;Aronson et al, 2017). Urban green space contributes to social cohesion and local identity (Kim, Kaplan, 2004;Roberts, 2017).…”

Section: Introductionmentioning

confidence: 99%

An analysis of the spatial distribution, influence and quality of urban green space – a case study of the Polish city of Tczew

Senetra

Krzywnicka²,

Mielke³

2018

Bulletin of Geography. Socio-Economic Series

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Rapid urban growth can exert negative effects on the natural environment due to the loss of naturally vegetated areas, loss of biological diversity, deforestation and soil erosion. The condition of cities is inherently linked with the natural environment which has a positive influence on health, social relations, human welfare and economic activity. Urban areas should abound in green spaces, and should also be easily accessible to the general public. The aim of this study was to determine the spatial distribution, influence and quality of urban green spaces on the example of the city of Tczew in northern Poland. The proposed methodology can be applied in cities of a similar size and urban structure to promote rational management of urban green space in line with the principles of sustainable development and spatial order. The Green-Space Record, a useful tool for inventorying urban green spaces, was developed to pursue the main research goal. The information accumulated in the Record constitutes valuable input data for further analysis, including the determination of the area, distribution, influence and quality of urban green spaces. The results of the analysis revealed that urban green spaces occupy more than 19% of Tczew’s territory, which is equivalent to 70.6 m2 per resident. Managed green spaces span the area of only 66.75 ha (11.31 m2 per resident) and are unevenly distributed in the city. More than half of these areas are found in the Stare Miasto (Old Town) district, whereas two residential districts (Gdańska, Prątnica) are completely devoid of public greens. The quality of urban green spaces is generally satisfactory in Tczew; however, not all residents have equal access to high-quality public greens.

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“…In [26], the authors studied human activity from mobile socio-demographic data in six Italian cities. Roberts [27] proposed the use of Twitter data for urban green space research. Time and geo-coordinates associated with a sequence of messages or tweets reflect the spatial and temporal movements of people in real life.…”

Section: Related Workmentioning

confidence: 99%

Urban Crowd Detection Using SOM, DBSCAN and LBSN Data Entropy: A Twitter Experiment in New York and Madrid

2019

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The surfer and the physical location are two important concepts associated with each other in the social network-based localization service. This work consists of studying urban behavior based on location-based social networks (LBSN) data; we focus especially on the detection of abnormal events. The proposed crowd detection system uses the geolocated social network provided by the Twitterapplication programming interface (API) to automatically detect the abnormal events. The methodology we propose consists of using an unsupervised competitive learning algorithm (self-organizing map (SOM)) and a density-based clustering method (density-based spatial clustering of applications with noise (DBCSAN)) to identify and detect crowds. The second stage is to build the entropy model to determine whether the detected crowds fit into the daily pattern with reference to a spatio-temporal entropy model, or whether they should be considered as evidence that something unusual occurs in the city because of their number, size, location and time of day. To detect an abnormal event in the city, it is sufficient to determine the real entropy model and to compare it with the reference model. For the normal day, the reference model is constructed offline for each time interval. The obtained results confirm the effectiveness of our method used in the first stage (SOM and DBSCAN stage) to detect and identify clusters dynamically, and imitating human activity. These findings also clearly confirm the detection of special days in New York City (NYC), which proves the performance of our proposed model.

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Using Twitter data in urban green space research: A case study and critical evaluation

Cited by 83 publications

References 42 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

An analysis of the spatial distribution, influence and quality of urban green space – a case study of the Polish city of Tczew

Urban Crowd Detection Using SOM, DBSCAN and LBSN Data Entropy: A Twitter Experiment in New York and Madrid

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